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  • 1.
    Abdul Khaliq, Ali
    et al.
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Point-to-point safe navigation of a mobile robot using stigmergy and RFID technology2016In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1497-1504, article id 7759243Conference paper (Refereed)
    Abstract [en]

    Reliable autonomous navigation is still a challenging problem for robots with simple and inexpensive hardware. A key difficulty is the need to maintain an internal map of the environment and an accurate estimate of the robot’s position in this map. Recently, a stigmergic approach has been proposed in which a navigation map is stored into the environment, on a grid of RFID tags, and robots use it to optimally reach predefined goal points without the need for internal maps. While effective,this approach is limited to a predefined set of goal points. In this paper, we extend this approach to enable robots to travel to any point on the RFID floor, even if it was not previously identified as a goal location, as well as to keep a safe distance from any given critical location. Our approach produces safe, repeatable and quasi-optimal trajectories without the use of internal maps, self localization, or path planning. We report experiments run in a real apartment equipped with an RFID floor, in which a service robot either reaches or avoids a user who wears slippers equipped with an RFID tag reader.

  • 2.
    Adolfsson, Daniel
    Örebro University, School of Science and Technology.
    Robust large-scale mapping and localization: Combining robust sensing and introspection2023Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The presence of autonomous systems is rapidly increasing in society and industry. To achieve successful, efficient, and safe deployment of autonomous systems, they must be navigated by means of highly robust localization systems. Additionally, these systems need to localize accurately and efficiently in realtime under adverse environmental conditions, and within considerably diverse and new previously unseen environments.

    This thesis focuses on investigating methods to achieve robust large-scale localization and mapping, incorporating robustness at multiple stages. Specifically, the research explores methods with sensory robustness, utilizing radar, which exhibits tolerance to harsh weather, dust, and variations in lighting conditions. Furthermore, the thesis presents methods with algorithmic robustness, which prevent failures by incorporating introspective awareness of localization quality. This thesis aims to answer the following research questions:

    How can radar data be efficiently filtered and represented for robust radar odometry? How can accurate and robust odometry be achieved with radar? How can localization quality be assessed and leveraged for robust detection of localization failures? How can self-awareness of localization quality be utilized to enhance the robustness of a localization system?

    While addressing these research questions, this thesis makes the following contributions to large-scale localization and mapping: A method for robust and efficient radar processing and state-of-the-art odometry estimation, and a method for self-assessment of localization quality and failure detection in lidar and radar localization. Self-assessment of localization quality is integrated into robust systems for large-scale Simultaneous Localization And Mapping, and rapid global localization in prior maps. These systems leverage self-assessment of localization quality to improve performance and prevent failures in loop closure and global localization, and consequently achieve safe robot localization.

    The methods presented in this thesis were evaluated through comparative assessments of public benchmarks and real-world data collected from various industrial scenarios. These evaluations serve to validate the effectiveness and reliability of the proposed approaches. As a result, this research represents a significant advancement toward achieving highly robust localization capabilities with broad applicability.

    List of papers
    1. Oriented surface points for efficient and accurate radar odometry
    Open this publication in new window or tab >>Oriented surface points for efficient and accurate radar odometry
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    2021 (English)Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper presents an efficient and accurate radar odometry pipeline for large-scale localization. We propose a radar filter that keeps only the strongest reflections per-azimuth that exceeds the expected noise level. The filtered radar data is used to incrementally estimate odometry by registering the current scan with a nearby keyframe. By modeling local surfaces, we were able to register scans by minimizing a point-to-line metric and accurately estimate odometry from sparse point sets, hence improving efficiency. Specifically, we found that a point-to-line metric yields significant improvements compared to a point-to-point metric when matching sparse sets of surface points. Preliminary results from an urban odometry benchmark show that our odometry pipeline is accurate and efficient compared to existing methods with an overall translation error of 2.05%, down from 2.78% from the previously best published method, running at 12.5ms per frame without need of environmental specific training. 

    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:oru:diva-108799 (URN)
    Conference
    Radar Perception for All-Weather Autonomy - Half-Day Workshop at 2021 IEEE International Conference on Robotics and Automation (ICRA 2021), Xi'an, China, May 30 - June 5, 2021
    Funder
    Knowledge FoundationEU, Horizon 2020, 732737
    Available from: 2023-10-09 Created: 2023-10-09 Last updated: 2024-01-02Bibliographically approved
    2. CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry
    Open this publication in new window or tab >>CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry
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    2021 (English)In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), IEEE, 2021, p. 5462-5469Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation. By using a filtering technique that keeps the k strongest returns per azimuth and by additionally filtering the radar data in Cartesian space, we are able to compute a sparse set of oriented surface points for efficient and accurate scan matching. Registration is carried out by minimizing a point-to-line metric and robustness to outliers is achieved using a Huber loss. We were able to additionally reduce drift by jointly registering the latest scan to a history of keyframes and found that our odometry method generalizes to different sensor models and datasets without changing a single parameter. We evaluate our method in three widely different environments and demonstrate an improvement over spatially cross-validated state-of-the-art with an overall translation error of 1.76% in a public urban radar odometry benchmark, running at 55Hz merely on a single laptop CPU thread.

    Place, publisher, year, edition, pages
    IEEE, 2021
    Series
    IEEE International Conference on Intelligent Robots and Systems. Proceedings, ISSN 2153-0858, E-ISSN 2153-0866
    Keywords
    Localization SLAM Mapping Radar
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-94463 (URN)10.1109/IROS51168.2021.9636253 (DOI)000755125504051 ()9781665417143 (ISBN)9781665417150 (ISBN)
    Conference
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), Prague, Czech Republic, (Online Conference), September 27 - October 1, 2021
    Funder
    Knowledge FoundationEU, Horizon 2020, 732737
    Available from: 2021-09-20 Created: 2021-09-20 Last updated: 2024-01-02Bibliographically approved
    3. Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments
    Open this publication in new window or tab >>Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments
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    2023 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 39, no 2, p. 1476-1495Article in journal (Refereed) Published
    Abstract [en]

    This article presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments—outdoors, from urban to woodland, and indoors in warehouses and mines—without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach conservative filtering for efficient and accurate radar odometry (CFEAR), we present an in-depth investigation on a wider range of datasets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar simultaneous localization and mapping (SLAM) and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5 Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160 Hz.

    Place, publisher, year, edition, pages
    IEEE, 2023
    Keywords
    Radar, Sensors, Spinning, Azimuth, Simultaneous localization and mapping, Estimation, Location awareness, Localization, radar odometry, range sensing, SLAM
    National Category
    Computer Sciences Computer Vision and Robotics (Autonomous Systems) Robotics
    Research subject
    Computer and Systems Science; Computer Science
    Identifiers
    urn:nbn:se:oru:diva-103116 (URN)10.1109/tro.2022.3221302 (DOI)000912778500001 ()2-s2.0-85144032264 (Scopus ID)
    Available from: 2023-01-16 Created: 2023-01-16 Last updated: 2023-10-18
    4. BFAR – Bounded False Alarm Rate detector for improved radar odometry estimation
    Open this publication in new window or tab >>BFAR – Bounded False Alarm Rate detector for improved radar odometry estimation
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    2021 (English)Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper presents a new detector for filtering noise from true detections in radar data, which improves the state of the art in radar odometry. Scanning Frequency-Modulated Continuous Wave (FMCW) radars can be useful for localisation and mapping in low visibility, but return a lot of noise compared to (more commonly used) lidar, which makes the detection task more challenging. Our Bounded False-Alarm Rate (BFAR) detector is different from the classical Constant False-Alarm Rate (CFAR) detector in that it applies an affine transformation on the estimated noise level after which the parameters that minimize the estimation error can be learned. BFAR is an optimized combination between CFAR and fixed-level thresholding. Only a single parameter needs to be learned from a training dataset. We apply BFAR tothe use case of radar odometry, and adapt a state-of-the-art odometry pipeline (CFEAR), replacing its original conservative filtering with BFAR. In this way we reduce the state-of-the-art translation/rotation odometry errors from 1.76%/0.5◦/100 m to 1.55%/0.46◦/100 m; an improvement of 12.5%.

    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:oru:diva-108800 (URN)
    Conference
    ICRA
    Funder
    Knowledge Foundation
    Available from: 2023-10-09 Created: 2023-10-09 Last updated: 2024-01-02Bibliographically approved
    5. CorAl – Are the point clouds Correctly Aligned?
    Open this publication in new window or tab >>CorAl – Are the point clouds Correctly Aligned?
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    2021 (English)In: 10th European Conference on Mobile Robots (ECMR 2021), IEEE, 2021, Vol. 10Conference paper, Published paper (Refereed)
    Abstract [en]

    In robotics perception, numerous tasks rely on point cloud registration. However, currently there is no method that can automatically detect misaligned point clouds reliably and without environment-specific parameters. We propose "CorAl", an alignment quality measure and alignment classifier for point cloud pairs, which facilitates the ability to introspectively assess the performance of registration. CorAl compares the joint and the separate entropy of the two point clouds. The separate entropy provides a measure of the entropy that can be expected to be inherent to the environment. The joint entropy should therefore not be substantially higher if the point clouds are properly aligned. Computing the expected entropy makes the method sensitive also to small alignment errors, which are particularly hard to detect, and applicable in a range of different environments. We found that CorAl is able to detect small alignment errors in previously unseen environments with an accuracy of 95% and achieve a substantial improvement to previous methods.

    Place, publisher, year, edition, pages
    IEEE, 2021
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:oru:diva-94464 (URN)10.1109/ECMR50962.2021.9568846 (DOI)000810510000059 ()
    Conference
    10th European Conference on Mobile Robots (ECMR 2021), Bonn, Germany, (Online Conference), August 31 - September 3, 2021
    Funder
    Knowledge FoundationEU, Horizon 2020, 732737 101017274
    Available from: 2021-09-22 Created: 2021-09-22 Last updated: 2024-01-02Bibliographically approved
    6. CorAl: Introspection for robust radar and lidar perception in diverse environments using differential entropy
    Open this publication in new window or tab >>CorAl: Introspection for robust radar and lidar perception in diverse environments using differential entropy
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    2022 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 155, article id 104136Article in journal (Refereed) Published
    Abstract [en]

    Robust perception is an essential component to enable long-term operation of mobile robots. It depends on failure resilience through reliable sensor data and pre-processing, as well as failure awareness through introspection, for example the ability to self-assess localization performance. This paper presents CorAl: a principled, intuitive, and generalizable method to measure the quality of alignment between pairs of point clouds, which learns to detect alignment errors in a self-supervised manner. CorAl compares the differential entropy in the point clouds separately with the entropy in their union to account for entropy inherent to the scene. By making use of dual entropy measurements, we obtain a quality metric that is highly sensitive to small alignment errors and still generalizes well to unseen environments. In this work, we extend our previous work on lidar-only CorAl to radar data by proposing a two-step filtering technique that produces high-quality point clouds from noisy radar scans. Thus, we target robust perception in two ways: by introducing a method that introspectively assesses alignment quality, and by applying it to an inherently robust sensor modality. We show that our filtering technique combined with CorAl can be applied to the problem of alignment classification, and that it detects small alignment errors in urban settings with up to 98% accuracy, and with up to 96% if trained only in a different environment. Our lidar and radar experiments demonstrate that CorAl outperforms previous methods both on the ETH lidar benchmark, which includes several indoor and outdoor environments, and the large-scale Oxford and MulRan radar data sets for urban traffic scenarios. The results also demonstrate that CorAl generalizes very well across substantially different environments without the need of retraining.

    Place, publisher, year, edition, pages
    Elsevier, 2022
    Keywords
    Radar, Introspection, Localization
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:oru:diva-100756 (URN)10.1016/j.robot.2022.104136 (DOI)000833416900001 ()2-s2.0-85132693467 (Scopus ID)
    Funder
    Knowledge FoundationEuropean Commission, 101017274Vinnova, 2019-05878
    Available from: 2022-08-24 Created: 2022-08-24 Last updated: 2024-01-02Bibliographically approved
    7. TBV Radar SLAM - Trust but Verify Loop Candidates
    Open this publication in new window or tab >>TBV Radar SLAM - Trust but Verify Loop Candidates
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    2023 (English)In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 8, no 6, p. 3613-3620Article in journal (Refereed) Published
    Abstract [en]

    Robust SLAM in large-scale environments requires fault resilience and awareness at multiple stages, from sensing and odometry estimation to loop closure. In this work, we present TBV (Trust But Verify) Radar SLAM, a method for radar SLAM that introspectively verifies loop closure candidates. TBV Radar SLAM achieves a high correct-loop-retrieval rate by combining multiple place-recognition techniques: tightly coupled place similarity and odometry uncertainty search, creating loop descriptors from origin-shifted scans, and delaying loop selection until after verification. Robustness to false constraints is achieved by carefully verifying and selecting the most likely ones from multiple loop constraints. Importantly, the verification and selection are carried out after registration when additional sources of loop evidence can easily be computed. We integrate our loop retrieval and verification method with a robust odometry pipeline within a pose graph framework. By evaluation on public benchmarks we found that TBV Radar SLAM achieves 65% lower error than the previous state of the art. We also show that it generalizes across environments without needing to change any parameters. We provide the open-source implementation at https://github.com/dan11003/tbv_slam_public

    Place, publisher, year, edition, pages
    IEEE, 2023
    Keywords
    SLAM, localization, radar, introspection
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:oru:diva-106249 (URN)10.1109/LRA.2023.3268040 (DOI)000981889200013 ()2-s2.0-85153499426 (Scopus ID)
    Funder
    Vinnova, 2021-04714 2019-05878
    Available from: 2023-06-13 Created: 2023-06-13 Last updated: 2024-01-17Bibliographically approved
    8. Localising Faster: Efficient and precise lidar-based robot localisation in large-scale environments
    Open this publication in new window or tab >>Localising Faster: Efficient and precise lidar-based robot localisation in large-scale environments
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    2020 (English)In: 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2020, p. 4386-4392Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper proposes a novel approach for global localisation of mobile robots in large-scale environments. Our method leverages learning-based localisation and filtering-based localisation, to localise the robot efficiently and precisely through seeding Monte Carlo Localisation (MCL) with a deeplearned distribution. In particular, a fast localisation system rapidly estimates the 6-DOF pose through a deep-probabilistic model (Gaussian Process Regression with a deep kernel), then a precise recursive estimator refines the estimated robot pose according to the geometric alignment. More importantly, the Gaussian method (i.e. deep probabilistic localisation) and nonGaussian method (i.e. MCL) can be integrated naturally via importance sampling. Consequently, the two systems can be integrated seamlessly and mutually benefit from each other. To verify the proposed framework, we provide a case study in large-scale localisation with a 3D lidar sensor. Our experiments on the Michigan NCLT long-term dataset show that the proposed method is able to localise the robot in 1.94 s on average (median of 0.8 s) with precision 0.75 m in a largescale environment of approximately 0.5 km 2.

    Place, publisher, year, edition, pages
    IEEE, 2020
    Series
    IEEE International Conference on Robotics and Automation (ICRA), ISSN 1050-4729, E-ISSN 2577-087X
    Keywords
    Gaussian processes, learning (artificial intelligence), mobile robots, Monte Carlo methods, neural nets, optical radar, path planning, recursive estimation, robot vision, SLAM (robots), precise lidar-based robot localisation, large-scale environments, global localisation, Monte Carlo Localisation, MCL, fast localisation system, deep-probabilistic model, Gaussian process regression, deep kernel, precise recursive estimator, Gaussian method, deep probabilistic localisation, large-scale localisation, largescale environment, time 0.8 s, size 0.75 m, Robots, Neural networks, Three-dimensional displays, Laser radar, Kernel
    National Category
    Robotics
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-88030 (URN)10.1109/ICRA40945.2020.9196708 (DOI)000712319503010 ()2-s2.0-85092712554 (Scopus ID)978-1-7281-7396-2 (ISBN)978-1-7281-7395-5 (ISBN)
    Conference
    2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, May 31 - August 31, 2020
    Funder
    EU, Horizon 2020, 732737
    Note

    Funding agency:

    UK Research & Innovation (UKRI)

    Engineering & Physical Sciences Research Council (EPSRC) EP/M019918/1

    Available from: 2021-01-31 Created: 2021-01-31 Last updated: 2024-01-02Bibliographically approved
    9. NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation
    Open this publication in new window or tab >>NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation
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    2021 (English)In: 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2021Conference paper, Published paper (Refereed)
    Abstract [en]

    3D point cloud-based place recognition is highly demanded by autonomous driving in GPS-challenged environments and serves as an essential component (i.e. loop-closure detection) in lidar-based SLAM systems. This paper proposes a novel approach, named NDT-Transformer, for real-time and large-scale place recognition using 3D point clouds. Specifically, a 3D Normal Distribution Transform (NDT) representation is employed to condense the raw, dense 3D point cloud as probabilistic distributions (NDT cells) to provide the geometrical shape description. Then a novel NDT-Transformer network learns a global descriptor from a set of 3D NDT cell representations. Benefiting from the NDT representation and NDT-Transformer network, the learned global descriptors are enriched with both geometrical and contextual information. Finally, descriptor retrieval is achieved using a query-database for place recognition. Compared to the state-of-the-art methods, the proposed approach achieves an improvement of 7.52% on average top 1 recall and 2.73% on average top 1% recall on the Oxford Robotcar benchmark.

    Place, publisher, year, edition, pages
    IEEE, 2021
    Series
    IEEE International Conference on Robotics and Automation (ICRA), ISSN 1050-4729, E-ISSN 2577-087X
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-96652 (URN)10.1109/ICRA48506.2021.9560932 (DOI)000765738804041 ()2-s2.0-85124680724 (Scopus ID)9781728190778 (ISBN)9781728190785 (ISBN)
    Conference
    2021 IEEE International Conference on Robotics and Automation (ICRA 2021), Xi'an, China, May 30 - June 5, 2021
    Funder
    EU, Horizon 2020, 732737
    Note

    Funding agencies:

    UK Research & Innovation (UKRI)

    Engineering & Physical Sciences Research Council (EPSRC) EP/R026092/1  

    Royal Society of London European Commission RGS202432

    Available from: 2022-01-24 Created: 2022-01-24 Last updated: 2024-01-02Bibliographically approved
    10. Improving Localisation Accuracy using Submaps in warehouses
    Open this publication in new window or tab >>Improving Localisation Accuracy using Submaps in warehouses
    2018 (English)Conference paper, Oral presentation with published abstract (Other academic)
    Abstract [en]

    This paper presents a method for localisation in hybrid metric-topological maps built using only local information that is, only measurements that were captured by the robot when it was in a nearby location. The motivation is that observations are typically range and viewpoint dependent and that a map a discrete map representation might not be able to explain the full structure within a voxel. The localisation system uses a method to select submap based on how frequently and where from each submap was updated. This allow the system to select the most descriptive submap, thereby improving the localisation and increasing performance by up to 40%.

    National Category
    Robotics
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-71844 (URN)
    Conference
    IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Workshop on Robotics for Logistics in Warehouses and Environments Shared with Humans, Madrid, Spain, October 5, 2018
    Projects
    Iliad
    Available from: 2019-01-28 Created: 2019-01-28 Last updated: 2024-01-02Bibliographically approved
    11. A Submap per Perspective: Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality
    Open this publication in new window or tab >>A Submap per Perspective: Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality
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    2019 (English)In: 2019 European Conference on Mobile Robots (ECMR), IEEE, 2019Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper targets high-precision robot localization. We address a general problem for voxel-based map representations that the expressiveness of the map is fundamentally limited by the resolution since integration of measurements taken from different perspectives introduces imprecisions, and thus reduces localization accuracy.We propose SuPer maps that contain one Submap per Perspective representing a particular view of the environment. For localization, a robot then selects the submap that best explains the environment from its perspective. We propose SuPer mapping as an offline refinement step between initial SLAM and deploying autonomous robots for navigation. We evaluate the proposed method on simulated and real-world data that represent an important use case of an industrial scenario with high accuracy requirements in an repetitive environment. Our results demonstrate a significantly improved localization accuracy, up to 46% better compared to localization in global maps, and up to 25% better compared to alternative submapping approaches.

    Place, publisher, year, edition, pages
    IEEE, 2019
    National Category
    Computer Sciences
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-79739 (URN)10.1109/ECMR.2019.8870941 (DOI)000558081900037 ()2-s2.0-85074443858 (Scopus ID)978-1-7281-3605-9 (ISBN)
    Conference
    European Conference on Mobile Robotics (ECMR), Prague, Czech Republic, September 4-6, 2019
    Funder
    EU, Horizon 2020, 732737Knowledge Foundation
    Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2024-01-02Bibliographically approved
    12. Incorporating Ego-motion Uncertainty Estimates in Range Data Registration
    Open this publication in new window or tab >>Incorporating Ego-motion Uncertainty Estimates in Range Data Registration
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    2017 (English)In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1389-1395Conference paper, Published paper (Refereed)
    Abstract [en]

    Local scan registration approaches commonlyonly utilize ego-motion estimates (e.g. odometry) as aninitial pose guess in an iterative alignment procedure. Thispaper describes a new method to incorporate ego-motionestimates, including uncertainty, into the objective function of aregistration algorithm. The proposed approach is particularlysuited for feature-poor and self-similar environments,which typically present challenges to current state of theart registration algorithms. Experimental evaluation showssignificant improvements in accuracy when using data acquiredby Automatic Guided Vehicles (AGVs) in industrial productionand warehouse environments.

    Place, publisher, year, edition, pages
    Institute of Electrical and Electronics Engineers (IEEE), 2017
    Series
    Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems, ISSN 2153-0858, E-ISSN 2153-0866
    National Category
    Robotics
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-62803 (URN)10.1109/IROS.2017.8202318 (DOI)000426978201108 ()2-s2.0-85041958720 (Scopus ID)978-1-5386-2682-5 (ISBN)978-1-5386-2683-2 (ISBN)
    Conference
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), Vancouver, Canada, September 24–28, 2017
    Projects
    Semantic RobotsILIAD
    Funder
    Knowledge FoundationEU, Horizon 2020, 732737
    Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2024-01-02Bibliographically approved
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  • 3.
    Adolfsson, Daniel
    et al.
    Örebro University, School of Science and Technology.
    Lowry, Stephanie
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    A Submap per Perspective: Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality2019In: 2019 European Conference on Mobile Robots (ECMR), IEEE, 2019Conference paper (Refereed)
    Abstract [en]

    This paper targets high-precision robot localization. We address a general problem for voxel-based map representations that the expressiveness of the map is fundamentally limited by the resolution since integration of measurements taken from different perspectives introduces imprecisions, and thus reduces localization accuracy.We propose SuPer maps that contain one Submap per Perspective representing a particular view of the environment. For localization, a robot then selects the submap that best explains the environment from its perspective. We propose SuPer mapping as an offline refinement step between initial SLAM and deploying autonomous robots for navigation. We evaluate the proposed method on simulated and real-world data that represent an important use case of an industrial scenario with high accuracy requirements in an repetitive environment. Our results demonstrate a significantly improved localization accuracy, up to 46% better compared to localization in global maps, and up to 25% better compared to alternative submapping approaches.

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    A Submap per Perspective - Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality
  • 4.
    Adolfsson, Daniel
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Alhashimi, Anas
    Örebro University, Örebro, Sweden; Computer Engineering Department, University of Baghdad, Baghdad, Iraq.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments2023In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 39, no 2, p. 1476-1495Article in journal (Refereed)
    Abstract [en]

    This article presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments—outdoors, from urban to woodland, and indoors in warehouses and mines—without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach conservative filtering for efficient and accurate radar odometry (CFEAR), we present an in-depth investigation on a wider range of datasets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar simultaneous localization and mapping (SLAM) and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5 Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160 Hz.

    Download full text (pdf)
    Lidar-level localization with radar? The CFEAR approach to accurate, fast and robust large-scale radar odometry in diverse environments
  • 5.
    Adolfsson, Daniel
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Alhashimi, Anas
    School of Science and Technology, Örebro University, Örebro, Sweden.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Oriented surface points for efficient and accurate radar odometry2021Conference paper (Refereed)
    Abstract [en]

    This paper presents an efficient and accurate radar odometry pipeline for large-scale localization. We propose a radar filter that keeps only the strongest reflections per-azimuth that exceeds the expected noise level. The filtered radar data is used to incrementally estimate odometry by registering the current scan with a nearby keyframe. By modeling local surfaces, we were able to register scans by minimizing a point-to-line metric and accurately estimate odometry from sparse point sets, hence improving efficiency. Specifically, we found that a point-to-line metric yields significant improvements compared to a point-to-point metric when matching sparse sets of surface points. Preliminary results from an urban odometry benchmark show that our odometry pipeline is accurate and efficient compared to existing methods with an overall translation error of 2.05%, down from 2.78% from the previously best published method, running at 12.5ms per frame without need of environmental specific training. 

  • 6.
    Afshar, Sara
    et al.
    Mälardalen University, Västerås, Sweden.
    Nemati, Farhang
    Mälardalen University, Västerås, Sweden.
    Nolte, Thomas
    Mälardalen University, Västerås, Sweden.
    Resource Sharing under Multiprocessor Semi-Partitioned Scheduling2012In: 2012 IEEE International Conference on Embedded and Real-Time Computing Systems and Applications: Proceedings, IEEE, 2012, p. 290-299Conference paper (Refereed)
    Abstract [en]

    Semi-partitioned scheduling has become the subject of recent interest for multiprocessors due to better utilization results, compared to conventional global and partitioned scheduling algorithms. Under semi-partitioned scheduling, a major group of tasks are assigned to fixed processors while a low number of tasks are allocated to more than one processor. Various task assigning techniques have recently been proposed in a semi-partitioned environment. However, a synchronization mechanism for resource sharing among tasks in semi-partitioned scheduling has not yet been investigated. In this paper we propose and evaluate two methods for handling resource sharing under semi-partitioned scheduling in multiprocessor platforms. The main challenge addressed in this paper is to serve the resource requests of tasks that are assigned to different processors.

  • 7.
    Afshar, Sara
    et al.
    Mälardalen University, Västerås, Sweden.
    Nemati, Farhang
    Mälardalen University, Västerås, Sweden.
    Nolte, Thomas
    Mälardalen University, Västerås, Sweden.
    Towards Resource Sharing under Multiprocessor Semi-Partitioned Scheduling2012In: 7th IEEE International Symposium on Industrial Embedded Systems (SIES'12): Conference Proceedings, IEEE, 2012, p. 315-318Conference paper (Refereed)
    Abstract [en]

    Semi-partitioned scheduling has been the subject of recent interest, compared with conventional global and partitioned scheduling algorithms for multiprocessors, due to better utilization results. In semi-partitioned scheduling most tasks are assigned to fixed processors while a low number of tasks are split up and allocated to different processors. Various techniques have recently been proposed to assign tasks in a semi-partitioned environment. However, an appropriate resource sharing mechanism for handling the resource requests between tasks in semi-partitioned scheduling has not yet been investigated. In this paper we propose two methods for handling resource sharing under semi-partitioned scheduling in multiprocessor platforms. The main challenge is to handle the resource requests of tasks that are split over multiple processors.

  • 8.
    Aghazadeh, David
    Örebro University, School of Science and Technology.
    Utvärdering av tidsplaneringsverktyg för universitet2021Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Scheduling is most often a problem that occurs at schools and universities. Scheduling is a task where all activities must be assigned to time and space with the right resources. When scheduling is done manually, the task takes a long time and is difficult to solve. The time it takes to solve the task can take a large amount of time. With the help of the right tools, time can be shortened, a better schedule can be created, and the workforce can be placed elsewhere. Scheduling at Örebro University is done manually by the administration. The purpose of the project is to generate a ground for making decisions about tools that can be facilitating. The project's focus is on the timetabling problem for universities, not for schools or production planning. During the project, scheduling tools have been found and selected, with the requirement that they have open source code. For evaluation, I set out criteria I applied as the basis for comparison. The experiment design consisted of creating three different sample data to experiment on the tools. The purpose of the three different test data was to produce an assessment according to the points of the criteria. The tools that were most convincing were those that got the best results according to the assessment criteria.

  • 9.
    Agrawal, Vikas
    et al.
    IBM Research, , India.
    Archibald, Christopher
    Mississippi State University, Starkville, United States.
    Bhatt, Mehul
    University of Bremen, Bremen, Germany.
    Bui, Hung Hai
    Laboratory for Natural Language Understanding, Sunnyvale CA, United States.
    Cook, Diane J.
    Washington State University, Pullman WA, United States.
    Cortés, Juan
    University of Toulouse, Toulouse, France.
    Geib, Christopher W.
    Drexel University, Philadelphia PA, United States.
    Gogate, Vibhav
    Department of Computer Science, University of Texas, Dallas, United States.
    Guesgen, Hans W.
    Massey University, Palmerston North, New Zealand.
    Jannach, Dietmar
    Technical university Dortmund, Dortmund, Germany.
    Johanson, Michael
    University of Alberta, Edmonton, Canada.
    Kersting, Kristian
    Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme (IAIS), Sankt Augustin, Germany; The University of Bonn, Bonn, Germany.
    Konidaris, George
    Massachusetts Institute of Technology (MIT), Cambridge MA, United States.
    Kotthoff, Lars
    INSIGHT Centre for Data Analytics, University College Cork, Cork, Ireland.
    Michalowski, Martin
    Adventium Labs, Minneapolis MN, United States.
    Natarajan, Sriraam
    Indiana University, Bloomington IN, United States.
    O’Sullivan, Barry
    INSIGHT Centre for Data Analytics, University College Cork, Cork, Ireland.
    Pickett, Marc
    Naval Research Laboratory, Washington DC, United States.
    Podobnik, Vedran
    Telecommunication Department of the Faculty of Electrical Engineering and Computing, University of University of Zagreb, Zagreb, Croatia.
    Poole, David
    Department of Computer Science, University of British Columbia, Vancouver, Canada.
    Shastri, Lokendra
    Infosys, , India.
    Shehu, Amarda
    George Mason University, Washington, United States.
    Sukthankar, Gita
    University of Central Florida, Orlando FL, United States.
    The AAAI-13 Conference Workshops2013In: The AI Magazine, ISSN 0738-4602, Vol. 34, no 4, p. 108-115Article in journal (Refereed)
    Abstract [en]

    The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14-15, 2013, at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context-Aware System Architectures (WS-13-05); Artificial Intelligence and Robotics Methods in Computational Biology (WS-13-06); Combining Constraint Solving with Mining and Learning (WS-13-07); Computer Poker and Imperfect Information (WS-13-08); Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS-13-09); Intelligent Robotic Systems (WS-13-10); Intelligent Techniques for Web Personalization and Recommendation (WS-13-11); Learning Rich Representations from Low-Level Sensors (WS-13-12); Plan, Activity,, and Intent Recognition (WS-13-13); Space, Time, and Ambient Intelligence (WS-13-14); Trading Agent Design and Analysis (WS-13-15); and Statistical Relational Artificial Intelligence (WS-13-16)

  • 10.
    Agélii Genlott, Annika
    et al.
    Örebro University, Örebro University School of Business.
    Grönlund, Åke
    Örebro University, Örebro University School of Business.
    Closing the gaps: Improving literacy and mathematics by ict-enhanced collaboration2016In: Computers and education, ISSN 0360-1315, E-ISSN 1873-782X, Vol. 99, p. 68-80Article in journal (Refereed)
    Abstract [en]

    Literacy and mathematics are necessary skills that for different reasons unfortunately not everybody acquires sufficiently. In OECD countries there is also a gender gap; boys lag behind girls in literacy but often outperform girls in mathematics (OECD, 2012). ICT (Information and communication technologies) may contribute useful tools to address both these problems but in order to effectively create better educational conditions there is yet a need to develop effective methods that combine ICT with key factors for learning. This research contributes to this by measuring effects of the “Write to Learn” (WTL) method. WTL lets children from 1st grade use several ICT tools to write texts and subsequently discuss and refine them together with classmates and teachers using digital real-time formative feedback and assessment. The central learning factor addressed, in mathematics as well as in literacy, is the written communication allowing the learners to interact with peers and teachers. WTL draws on methods from socio-cultural theory, including continuous social interaction and written real-time formative feedback among peers, using shared electronic forums for collaboration, thereby providing social meaning and increased learning of literacy and mathematics, among both boys and girls.

    The study uses quantitative methods and two control groups, one using traditional method (no ICT) and one using technology individually (without integrated social interaction and formative feedback), to compare results from 502 students in grade 3 national tests in mathematics and literacy. WTL yields by far best results; higher average score both in literacy and mathematics, smaller gender gap, and significantly better results for the under-achievers. The ITU method performs worst, which shows that ICT use must be well integrated into the pedagogy to be useful.

  • 11.
    Ahlberg, Ernst
    et al.
    Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Winiwarter, Susanne
    Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Boström, Henrik
    Department of Computer and Systems Sciences, Stockholm University, Sweden.
    Linusson, Henrik
    Department of Information Technology, University of Borås, Sweden.
    Löfström, Tuve
    Högskolan i Jönköping, JTH. Forskningsmiljö Datavetenskap och informatik, Jönköping, Sweden.
    Norinder, Ulf
    Swetox, Karolinska Institutet, Unit of Toxicology Sciences, Stockholm, Sweden.
    Johansson, Ulf
    Högskolan i Jönköping, JTH, Datateknik och informatik, Jönköping, Sweden.
    Engkvist, Ola
    External Sciences, Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Hammar, Oscar
    Quantitative Biology, Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Bendtsen, Claus
    Quantitative Biology, Discovery Sciences, AstraZeneca IMED Biotech Unit, Cambridge, England.
    Carlsson, Lars
    Quantitative Biology, Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Using conformal prediction to prioritize compound synthesis in drug discovery2017In: Proceedings of Machine Learning Research: Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, and Harris Papadopoulos, Stockholm, 2017, p. 174-184Conference paper (Refereed)
    Abstract [en]

    The choice of how much money and resources to spend to understand certain problems is of high interest in many areas. This work illustrates how computational models can be more tightly coupled with experiments to generate decision data at lower cost without reducing the quality of the decision. Several different strategies are explored to illustrate the trade off between lowering costs and quality in decisions.

    AUC is used as a performance metric and the number of objects that can be learnt from is constrained. Some of the strategies described reach AUC values over 0.9 and outperforms strategies that are more random. The strategies that use conformal predictor p-values show varying results, although some are top performing.

    The application studied is taken from the drug discovery process. In the early stages of this process compounds, that potentially could become marketed drugs, are being routinely tested in experimental assays to understand the distribution and interactions in humans.

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    FULLTEXT01
  • 12.
    Ahmed, Farouq
    Örebro University, School of Science and Technology.
    Utökad automatisering av e-handel med shopify API2018Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Competition among sellers is rapidly increasing within e-commerce due to the decreasing barrier of entry. In order for a seller to be relevant, effectiveness is a requirement that can be achieved by using different methods and tools that result in a successful and profitable online store. One of the methods that sellers use is drop shipping which allows sale of goods without inventory holding. This is done by ordering the products from a third part that ships to the customer the moment an order is placed, instead of purchasing the products in advance. This leads to cheaper and more effective trade for new and already existing sellers.Despite the good opportunities that drop shipping allows, it is a time consuming task that requires a high level of accuracy due to the many repetitive tasks that are performed during the sales process. This is a problem that can hinder sellers from using the drop shipping model. In order to make it easier for sellers to utilize this method, a program can perform the manual tasks automatically.In this report, different software architectural patterns are studied to build the base of a prototype program that could perform the manual tasks with a satisfactory level of automation.

  • 13.
    Ahmed, Mobyen Uddin
    et al.
    Örebro University, School of Science and Technology.
    Banaee, Hadi
    Örebro University, School of Science and Technology.
    Rafael-Palou, Xavier
    Barcelona Digital Technology Centre, Barcelona, Spain.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Intelligent Healthcare Services to Support Health Monitoring of Elderly2015In: INTERNET OF THINGS: USER-CENTRIC IOT, PT I, Springer, 2015, Vol. 150, p. 178-186Conference paper (Refereed)
    Abstract [en]

    This paper proposed an approach of intelligent healthcare services to support health monitoring of old people through the project named SAAPHO. Here, definition and architecture of the proposed healthcare services are presented considering six different health parameters such as: 1) physical activity, 2) blood pressure, 3) glucose, 4) medication compliance, 5) pulse monitoring and 6) weight monitoring. The outcome of the proposed services is evaluated in a case study where total 201 subjects from Spain and Slovenia are involved for user requirements analysis considering 1) end users, 2) clinicians, and 3) field study analysis perspectives. The result shows the potentiality and competence of the proposed healthcare services for the users.

    Download full text (pdf)
    fulltext
  • 14.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, Västerås, Sweden.
    Fotouhi, Hossein
    Mälardalen University, Västerås, Sweden.
    Köckemann, Uwe
    Örebro University, School of Science and Technology.
    Lindén, Maria
    Mälardalen University, Västerås, Sweden.
    Tomasic, Ivan
    Mälardalen University, Västerås, Sweden.
    Tsiftes, Nicolas
    RISE SICS, Stockholm, Sweden.
    Voigt, Thiemo
    RISE SICS, Stockholm, Sweden.
    Run-Time Assurance for the E-care@home System2018In: Internet of Things (IoT) Technologies for HealthCare (HealthyIoT 2017) / [ed] Ahmed, MU; Begum, S; Fasquel, JB, Springer, 2018, Vol. 225, p. 107-110Conference paper (Refereed)
    Abstract [en]

    This paper presents the design and implementation of the software for a run-time assurance infrastructure in the E-care home system. An experimental evaluation is conducted to verify that the run-time assurance infrastructure is functioning correctly, and to enable detecting performance degradation in experimental IoT network deployments within the context of E-care home.

  • 15.
    Ahmed, Mobyen Uddin
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Physical Activity Classification for Elderly based on Pulse Rate2013Conference paper (Refereed)
    Abstract [en]

    Physical activity is one of the key components for elderly in order to be actively ageing. However, it is difficult to differentiate and identify the body movement and actual physical activity using only accelerometer measurement. Therefore, this paper presents an application of case-based retrieval classification scheme to classify the physical activity of elderly based on pulse rate measurements. Here, case-based retrieval approach used the features extracted from both time and frequency domain. The evaluation result shows the best accuracy performance while considering the combination of time and frequency domain features. According to the evaluation result while considering the control measurements, the sensitivity, specificity and overall accuracy are achieved as 95%, 96% and 96% respectively. Considering the test dataset, the system was succeeded to identify 13 physical activities out of 16 i.e. the percentage of the correctness was 81%.

  • 16.
    Ahmed, Mobyen Uddin
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Physical activity identification using supervised machine learning and based on pulse rate2013In: International Journal of Advanced Computer Sciences and Applications, ISSN 2158-107X, E-ISSN 2156-5570, Vol. 4, no 7, p. 210-217Article in journal (Refereed)
    Abstract [en]

    Physical activity is one of the key components for elderly in order to be actively ageing. Pulse rate is a convenient physiological parameter to identify elderly’s physical activity since it increases with activity and decreases with rest. However, analysis and classification of pulse rate is often difficult due to personal variation during activity. This paper proposed a Case-Based Reasoning (CBR) approach to identify physical activity of elderly based on pulse rate. The proposed CBR approach has been compared with the two popular classification techniques, i.e. Support Vector Machine (SVM) and Neural Network (NN). The comparison has been conducted through an empirical experimental study where three experiments with 192 pulse rate measurement data are used. The experiment result shows that the proposed CBR approach outperforms the other two methods. Finally, the CBR approach identifies physical activity of elderly 84% accurately based on pulse rate

    Download full text (pdf)
    Physical activity identification using supervised machine learning and based on pulse rate
  • 17.
    Ahmed, Rehan M.
    et al.
    Örebro University, School of Science and Technology.
    Ananiev, Anani V.
    Örebro University, School of Science and Technology.
    Kalaykov, Ivan
    Örebro University, School of Science and Technology.
    Compliant motion control for safe human robot interaction2009In: Robot motion and control 2009 / [ed] Krzysztof R. Kozłowski, Berlin: Springer , 2009, p. 265-274Conference paper (Refereed)
    Abstract [en]

    Robots have recently been foreseen to work side by side and share workspace with humans in assisting them in tasks that include physical human-robot (HR) interaction. The physical contact with human tasks under uncertainty has to be performed in a stable and safe manner [6]. However, current industrial robot manipulators are still very far from HR coexisting environments, because of their unreliable safety, rigidity and heavy structure. Besides this, the industrial norms separate the two spaces occupied by a human and a robot by means of physical fence or wall [9]. Therefore, the success of such physical HR interaction is possible if the robot is enabled to handle this interaction in a smart way to prevent injuries and damages.

  • 18.
    Ahmed, Rehan M.
    et al.
    Örebro University, School of Science and Technology.
    Kalaykov, Ivan
    Örebro University, School of Science and Technology.
    Ananiev, Anani
    Örebro University, School of Science and Technology.
    Modeling of magneto rheological fluid actuator enabling safe human-robot interaction2008In: IEEE International Conference on Emerging Technologies and Factory Automation, 2008. ETFA 2008, 2008, p. 974-979Conference paper (Refereed)
    Abstract [en]

    Impedance control and compliant behavior for safe human-robot physical interaction of industrial robots normally can be achieved by using active compliance control of actuators based on various sensor data. Alternatively, passive devices allow controllable compliance motion but usually are mechanically complex. We present another approach using a novel actuation mechanism based on magneto-rheological fluid (MRF) that incorporates variable stiffness directly into the joints. In this paper, we have investigated and analyzed principle characteristics of MRF actuation mechanism and presented the analytical-model. Then we have developed the static and dynamic model based on experimental test results and have discussed three essential modes of motion needed for human-robot manipulation interactive tasks.

  • 19.
    Ahnaf, S.M. Azoad
    et al.
    Computational Color and Spectral Image Analysis Lab, Computer Science and Engineering, Discipline Khulna University, Khulna, Bangladesh.
    Rahaman, G. M. Atiqur
    Computational Color and Spectral Image Analysis Lab, Computer Science and Engineering, Discipline Khulna University, Khulna, Bangladesh.
    Saha, Sajib
    Australian e-health Research Centre, CSIRO, Perth, Australia.
    Understanding CNN's Decision Making on OCT-based AMD Detection2021In: 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), 14-16 Sept. 2021, IEEE, 2021, p. 1-4Conference paper (Refereed)
    Abstract [en]

    Age-related Macular degeneration (AMD) is the third leading cause of incurable acute central vision loss. Optical coherence tomography (OCT) is a diagnostic process used for both AMD and diabetic macular edema (DME) detection. Spectral-domain OCT (SD-OCT), an improvement of traditional OCT, has revolutionized assessing AMD for its high acquiring rate, high efficiency, and resolution. To detect AMD from normal OCT scans many techniques have been adopted. Automatic detection of AMD has become popular recently. The use of a deep Convolutional Neural Network (CNN) has helped its cause vastly. Despite having achieved better performance, CNN models are often criticized for not giving any justification in decision-making. In this paper, we aim to visualize and critically analyze the decision of CNNs in context-based AMD detection. Multiple experiments were done using the DUKE OCT dataset, utilizing transfer learning in Resnet50 and Vgg16 model. After training the model for AMD detection, Gradient-weighted Class Activation Mapping (Grad-Cam) is used for feature visualization. With the feature mapped image, each layer mask was compared. We have found out that the Outer Nuclear layer to the Inner segment myeloid (ONL-ISM) has more predominance about 17.13% for normal and 6.64% for AMD in decision making.

  • 20.
    Ahtiainen, Juhana
    et al.
    Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Saarinen, Jari
    GIM Ltd., Espoo, Finland.
    Normal Distributions Transform Traversability Maps: LIDAR-Only Approach for Traversability Mapping in Outdoor Environments2017In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 34, no 3, p. 600-621Article in journal (Refereed)
    Abstract [en]

    Safe and reliable autonomous navigation in unstructured environments remains a challenge for field robots. In particular, operating on vegetated terrain is problematic, because simple purely geometric traversability analysis methods typically classify dense foliage as nontraversable. As traversing through vegetated terrain is often possible and even preferable in some cases (e.g., to avoid executing longer paths), more complex multimodal traversability analysis methods are necessary. In this article, we propose a three-dimensional (3D) traversability mapping algorithm for outdoor environments, able to classify sparsely vegetated areas as traversable, without compromising accuracy on other terrain types. The proposed normal distributions transform traversability mapping (NDT-TM) representation exploits 3D LIDAR sensor data to incrementally expand normal distributions transform occupancy (NDT-OM) maps. In addition to geometrical information, we propose to augment the NDT-OM representation with statistical data of the permeability and reflectivity of each cell. Using these additional features, we train a support-vector machine classifier to discriminate between traversable and nondrivable areas of the NDT-TM maps. We evaluate classifier performance on a set of challenging outdoor environments and note improvements over previous purely geometrical traversability analysis approaches.

  • 21.
    Akalin, Neziha
    Örebro University, School of Science and Technology.
    Perceived Safety in Social Human-Robot Interaction2022Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This compilation thesis contributes to a deeper understanding of perceived safety in human-robot interaction (HRI) with a particular focus on social robots. The current understanding of safety in HRI is mostly limited to physical safety, whereas perceived safety has often been neglected and underestimated. However, safe HRI requires a conceptualization of safety that goes beyond physical safety covering also perceived safety of the users. Within this context, this thesis provides a comprehensive analysis of perceived safety in HRI with social robots, considering a diverse set of human-related and robot-related factors.

    Two particular challenges for providing perceived safety in HRI are 1) understanding and evaluating human safety perception through direct and indirect measures, and 2) utilizing the measured level of perceived safety for adapting the robot behaviors. The primary contribution of this dissertation is in addressing the first challenge. The thesis investigates perceived safety in HRI by alternating between conducting user studies, literature review, and testing the findings from the literature within user studies.

    In this thesis, six main factors influencing perceived safety in HRI are lifted: the context of robot use, the user’s comfort, experience and familiarity with robots, trust, sense of control over the interaction, and transparent and predictable robot behaviors. These factors could provide a common understanding of perceived safety and bridge the theoretical gap in the literature. Moreover, this thesis proposes an experimental paradigm to observe and quantify perceived safety using objective and subjective measures. This contributes to bridging the methodological gap in the literature.

    The six factors are reviewed in HRI literature, and the robot features that affect these factors are organized in a taxonomy. Although this taxonomy focuses on social robots, the identified characteristics are relevant to other types of robots and autonomous systems. In addition to the taxonomy, the thesis provides a set of guidelines for providing perceived safety in social HRI. As a secondary contribution, the thesis presents an overview of reinforcement learning applications in social robotics as a suitable learning mechanism for adapting the robots’ behaviors to mitigate psychological harm.

    List of papers
    1. An Evaluation Tool of the Effect of Robots in Eldercare on the Sense of Safety and Security
    Open this publication in new window or tab >>An Evaluation Tool of the Effect of Robots in Eldercare on the Sense of Safety and Security
    2017 (English)In: Social Robotics: 9th International Conference, ICSR 2017, Tsukuba, Japan, November 22-24, 2017, Proceedings / [ed] Kheddar, A.; Yoshida, E.; Ge, S.S.; Suzuki, K.; Cabibihan, J-J:, Eyssel, F:, He, H., Springer International Publishing , 2017, p. 628-637Conference paper, Published paper (Refereed)
    Abstract [en]

    The aim of the study presented in this paper is to develop a quantitative evaluation tool of the sense of safety and security for robots in eldercare. By investigating the literature on measurement of safety and security in human-robot interaction, we propose new evaluation tools. These tools are semantic differential scale questionnaires. In experimental validation, we used the Pepper robot, programmed in the way to exhibit social behaviors, and constructed four experimental conditions varying the degree of the robot’s non-verbal behaviors from no gestures at all to full head and hand movements. The experimental results suggest that both questionnaires (for the sense of safety and the sense of security) have good internal consistency.

    Place, publisher, year, edition, pages
    Springer International Publishing, 2017
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10652
    Keywords
    Sense of safety, Sense of security, Eldercare, Video-based evaluation, Quantitative evaluation tool
    National Category
    Computer Systems Computer Vision and Robotics (Autonomous Systems)
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-62768 (URN)10.1007/978-3-319-70022-9_62 (DOI)000449941100062 ()2-s2.0-85035814295 (Scopus ID)978-3-319-70022-9 (ISBN)978-3-319-70021-2 (ISBN)
    Conference
    9th International Conference on Social Robotics (ICSR 2017), Tsukuba, Japan, November 22-24, 2017
    Projects
    SOCRATES
    Funder
    EU, Horizon 2020, 721619
    Available from: 2017-11-22 Created: 2017-11-22 Last updated: 2024-01-16Bibliographically approved
    2. Evaluating the Sense of Safety and Security in Human - Robot Interaction with Older People
    Open this publication in new window or tab >>Evaluating the Sense of Safety and Security in Human - Robot Interaction with Older People
    2019 (English)In: Social Robots: Technological, Societal and Ethical Aspects of Human-Robot Interaction / [ed] Oliver Korn, Springer, 2019, p. 237-264Chapter in book (Refereed)
    Abstract [en]

    For many applications where interaction between robots and older people takes place, safety and security are key dimensions to consider. ‘Safety’ refers to a perceived threat of physical harm, whereas ‘security’ is a broad term which refers to many aspects related to health, well-being, and aging. This chapter presents a quantitative evaluation tool of the sense of safety and security for robots in elder care. By investigating the literature on measurement of safety and security in human–robot interaction, we propose new evaluation tools specially tailored to assess interaction between robots and older people.

    Place, publisher, year, edition, pages
    Springer, 2019
    Series
    Human-Computer Interaction Series, ISSN 1571-5035, E-ISSN 2524-4477
    Keywords
    Sense of safety and security, Quantitative evaluation tool, Social robots, Elder care
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:oru:diva-78493 (URN)10.1007/978-3-030-17107-0_12 (DOI)978-3-030-17106-3 (ISBN)978-3-030-17107-0 (ISBN)
    Available from: 2019-12-08 Created: 2019-12-08 Last updated: 2024-01-16Bibliographically approved
    3. The Influence of Feedback Type in Robot-Assisted Training
    Open this publication in new window or tab >>The Influence of Feedback Type in Robot-Assisted Training
    2019 (English)In: Multimodal Technologies and Interaction, E-ISSN 2414-4088, Vol. 3, no 4Article in journal (Refereed) Published
    Abstract [en]

    Robot-assisted training, where social robots can be used as motivational coaches, provides an interesting application area. This paper examines how feedback given by a robot agent influences the various facets of participant experience in robot-assisted training. Specifically, we investigated the effects of feedback type on robot acceptance, sense of safety and security, attitude towards robots and task performance. In the experiment, 23 older participants performed basic arm exercises with a social robot as a guide and received feedback. Different feedback conditions were administered, such as flattering, positive and negative feedback. Our results suggest that the robot with flattering and positive feedback was appreciated by older people in general, even if the feedback did not necessarily correspond to objective measures such as performance. Participants in these groups felt better about the interaction and the robot.

    Place, publisher, year, edition, pages
    Multidisciplinary Digital Publishing Institute, 2019
    Keywords
    feedback, acceptance, flattering robot, sense of safety and security, robot-assisted training
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:oru:diva-78492 (URN)10.3390/mti3040067 (DOI)000623570700003 ()2-s2.0-85079720466 (Scopus ID)
    Funder
    EU, Horizon 2020, 721619
    Available from: 2019-12-08 Created: 2019-12-08 Last updated: 2024-01-16Bibliographically approved
    4. Do you feel safe with your robot? Factors influencing perceived safety in human-robot interaction based on subjective and objective measures
    Open this publication in new window or tab >>Do you feel safe with your robot? Factors influencing perceived safety in human-robot interaction based on subjective and objective measures
    2022 (English)In: International journal of human-computer studies, ISSN 1071-5819, E-ISSN 1095-9300, Vol. 158, article id 102744Article in journal (Refereed) Published
    Abstract [en]

    Safety in human-robot interaction can be divided into physical safety and perceived safety, where the later is still under-addressed in the literature. Investigating perceived safety in human-robot interaction requires a multidisciplinary perspective. Indeed, perceived safety is often considered as being associated with several common factors studied in other disciplines, i.e., comfort, predictability, sense of control, and trust. In this paper, we investigated the relationship between these factors and perceived safety in human-robot interaction using subjective and objective measures. We conducted a two-by-five mixed-subjects design experiment. There were two between-subjects conditions: the faulty robot was experienced at the beginning or the end of the interaction. The five within-subjects conditions correspond to (1) baseline, and the manipulations of robot behaviors to stimulate: (2) discomfort, (3) decreased perceived safety, (4) decreased sense of control and (5) distrust. The idea of triggering a deprivation of these factors was motivated by the definition of safety in the literature where safety is often defined by the absence of it. Twenty-seven young adult participants took part in the experiments. Participants were asked to answer questionnaires that measure the manipulated factors after within-subjects conditions. Besides questionnaire data, we collected objective measures such as videos and physiological data. The questionnaire results show a correlation between comfort, sense of control, trust, and perceived safety. Since these factors are the main factors that influence perceived safety, they should be considered in human-robot interaction design decisions. We also discuss the effect of individual human characteristics (such as personality and gender) that they could be predictors of perceived safety. We used the physiological signal data and facial affect from videos for estimating perceived safety where participants’ subjective ratings were utilized as labels. The data from objective measures revealed that the prediction rate was higher from physiological signal data. This paper can play an important role in the goal of better understanding perceived safety in human-robot interaction.

    Place, publisher, year, edition, pages
    Academic Press, 2022
    Keywords
    Perceived safety, Human robot interaction, Comfort, Sense of control, Trust, Physiological signal data, Facial expressions, Multidisciplinary perspective
    National Category
    Robotics
    Research subject
    Human-Computer Interaction; Computer Science
    Identifiers
    urn:nbn:se:oru:diva-95673 (URN)10.1016/j.ijhcs.2021.102744 (DOI)000782270600008 ()2-s2.0-85119702541 (Scopus ID)
    Available from: 2021-11-29 Created: 2021-11-29 Last updated: 2024-01-16Bibliographically approved
    5. Reinforcement Learning Approaches in Social Robotics
    Open this publication in new window or tab >>Reinforcement Learning Approaches in Social Robotics
    2021 (English)In: Sensors, E-ISSN 1424-8220, Vol. 21, no 4, article id 1292Article, review/survey (Refereed) Published
    Abstract [en]

    This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field.

    Place, publisher, year, edition, pages
    MDPI, 2021
    Keywords
    Human-robot interaction, physical embodiment, reinforcement learning, reward design, social robotics
    National Category
    Robotics
    Identifiers
    urn:nbn:se:oru:diva-90245 (URN)10.3390/s21041292 (DOI)000624663200001 ()33670257 (PubMedID)2-s2.0-85100651693 (Scopus ID)
    Funder
    EU, Horizon 2020, 721619
    Available from: 2021-03-08 Created: 2021-03-08 Last updated: 2024-01-16Bibliographically approved
    6. Guidelines for Identifying Factors Influencing Perceived Safety in Human-Robot Interaction
    Open this publication in new window or tab >>Guidelines for Identifying Factors Influencing Perceived Safety in Human-Robot Interaction
    (English)Manuscript (preprint) (Other academic)
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:oru:diva-98456 (URN)
    Available from: 2022-04-04 Created: 2022-04-04 Last updated: 2022-04-04Bibliographically approved
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    Perceived Safety in Social Human-Robot Interaction
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  • 22.
    Akalin, Neziha
    et al.
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Guidelines for Identifying Factors Influencing Perceived Safety in Human-Robot InteractionManuscript (preprint) (Other academic)
  • 23.
    Al Saleh, Alissar
    Örebro University, School of Science and Technology.
    Analysis and measurement of visuospatial complexity2023Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The thesis performs an analysis on visuospatial complexity of dynamic scenes, and morespecifically driving scenes in the propose of gaining a knowledge on human visual perception of the visual information present in a typical driving scene. The analysis and measurement of visual complexity is performed by utilizing two different measure modelsfor measuring visual clutter, Feature congestion clutter measure [1] and Subband entropyclutter measure[1] introduced by Rosenholtz, a cognitive science and research. The thesisrepresent the performance of the computational models on a data set consisting of sixepisodes that simulate driving scenes with different settings and combination of visualfeatures. The results of evaluating the measure models are used to introduce a formulafor measuring visual complexity of annotated images by extracting valuable informationfrom the annotated data set using Scalabel[2], an annotation web- based open source tool. 

    Download full text (pdf)
    fulltext
  • 24.
    Albitar, Houssam
    et al.
    Örebro University, School of Science and Technology.
    Ananiev, Anani
    Örebro University, School of Science and Technology.
    Kalaykov, Ivan
    Örebro University, School of Science and Technology.
    In-water surface cleaning robot: concept, locomotion and stability2014In: International Journal of Mechatronics and Automation, ISSN 2045-1067, Vol. 4, no 2, p. 104-115Article in journal (Refereed)
    Abstract [en]

    This paper introduces a new concept of flexible crawling mechanism in the design ofindustrial in-water cleaning robot, which is evaluated from the viewpoint of work and operationon an underwater surface. It enables the scanning and cleaning process performed by water jets,while keeping stable robot position on the surface by its capacity to bear and compensate the jetreactions. Such robotic platform can be used for cleaning and maintenance of various underwatersurfaces, including moving ships in the open sea. The designed robot implements its motions bycontraction and expansion of legged mechanism using standard motors and suction cupstechnology. In this study we focus at the conditions for achieving enough adhesion for keepingcontinuous contact between the robot and the surface and robot stability in different situations forthe basic locomotions.

  • 25.
    Albitar, Houssam
    et al.
    Örebro University, School of Science and Technology.
    Ananiev, Anani
    Örebro University, School of Science and Technology.
    Kalaykov, Ivan
    Örebro University, School of Science and Technology.
    New concept of in-water surface cleaning robot2013In: Mechatronics and Automation (ICMA), 2013 IEEE International Conference onDate 4-7 Aug. 2013, IEEE conference proceedings, 2013, p. 1582-1587Conference paper (Refereed)
    Abstract [en]

    This paper introduces a new concept of flexible crawling mechanism to design an industrial underwater cleaning robot, which is evaluated from the viewpoint of the capability to work underwater, scanning the desired surface, and bearing the reactions. This can be used as a robotic application in underwater surface cleaning and maintenance. We designed a robot that realizes the motion by contraction and extraction using DC-motors and vacuum technology. In this study we first focused on realizing the adhesion, bearing reactions, and achieving a stable locomotion on the surface.

    Download full text (pdf)
    ICMA2013-267
  • 26.
    Albitar, Houssam
    et al.
    Örebro University, School of Science and Technology.
    Ananiev, Anani
    Örebro University, School of Science and Technology.
    Kalaykov, Ivan
    Örebro University, School of Science and Technology.
    Stability study of underwater crawling robot on non-horizontal surface2014In: Mobile Service Robotics: Clawar 2014: 17th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines: Poznan, Poland 21 - 23 July 2014, Singapore: World Scientific, 2014, p. 511-519Conference paper (Refereed)
    Abstract [en]

    This paper introduces a study of a concept of exible crawling mechanism todesign an industrial underwater cleaning robot, which is evaluated from theviewpoint of the capability to work underwater, scanning the desired surface,and bearing the reactions. This can be used as a robotic application in under-water surface cleaning and maintenance. In this study we focused on realizingthe adhesion on the surface in stationary and in motion, bearing reactions,enabling the needed locomotion types for scanning, and achieving the stabilityin dierent situations on the surface.

    Download full text (pdf)
    fulltext
  • 27.
    Albitar, Houssam
    et al.
    Örebro University, School of Science and Technology.
    Dandan, Kinan
    Örebro University, School of Science and Technology.
    Ananiev, Anani
    Örebro University, School of Science and Technology.
    Kalaykov, Ivan
    Örebro University, School of Science and Technology.
    Layered mission control architecture and strategy for crawling underwater cleaning robot2015In: International Journal of Mechatronics and Automation, ISSN 2045-1059, Vol. 5, no 2/3, p. 114-124Article in journal (Refereed)
    Abstract [en]

    This paper presents the mechanical design and the control system architecture of anunderwater robot, developed for bio-fouling cleaning surfaces. The robotic system presented herehas been designed to improve the productivity, reduce the environmental impacts, and excludethe hazards for the operators. The control system has a layered structure which is distributed intotwo blocks: cleaning robot, and on-board base station connected with power and control cablesand a water hose, to facilitate different modes of operations and to increase the system reliability.A low level control has been implemented on the robotic platform. The onboard station designedto be in different layers of the control system: manual, semiautonomous and autonomous modes.A scaled prototype has been implemented and tested to prove the concept, and to make certainthat the mechanical design and the chosen control system are perfectly suited to the mainfunctions of the robotic system.

  • 28.
    Albitar, Houssam
    et al.
    Örebro University, School of Science and Technology.
    Dandan, Kinan
    Örebro University, School of Science and Technology.
    Ananiev, Anani
    Örebro University, School of Science and Technology.
    Kalaykov, Ivan
    Örebro University, School of Science and Technology.
    Underwater Robotics: Surface Cleaning Technics, Adhesion and Locomotion Systems2016In: International Journal of Advanced Robotic Systems, ISSN 1729-8806, E-ISSN 1729-8814, Vol. 13, article id 7Article in journal (Refereed)
    Abstract [en]

    Underwater robots are being developed for various applications ranging from inspection to maintenance and cleaning of submerged surfaces and constructions. These platforms should be able to travel on these surfaces. Furthermore, these platforms should adapt and reconfigure for underwater environment conditions and should be autonomous. Regarding the adhesion to the surface, they should produce a proper attaching force using a light-weight technics. Taking these facts into consideration, this paper presents a survey of different technologies used for underwater cleaning and the available underwater robotics solutions for the locomotion and the adhesion to surfaces.

  • 29.
    Aldammad, Mohamad
    et al.
    Örebro University, School of Science and Technology.
    Ananiev, Anani
    Örebro University, School of Science and Technology.
    Kalaykov, Ivan
    Örebro University, School of Science and Technology.
    Current collector for heavy vehicles on electrified roads2014In: Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014 / [ed] Leo J De Vin and Jorge Solis, Karlstad: Karlstads universitet , 2014, p. 436-441Conference paper (Refereed)
    Abstract [en]

    This paper presents a prototype of a novel current collector manipulator that can be mounted beneath a road vehicle between the front and rear wheels to collect electric power from road embedded power lines. The ground-level power supply concept for road vehicles is described and the kinematic model of this two degree of freedom manipulator is detailed. Finally, the power line detection, based on an array of inductive sensors, is discussed.

  • 30.
    Aldammad, Mohamad
    et al.
    Örebro University, School of Science and Technology.
    Ananiev, Anani
    Örebro University, School of Science and Technology.
    Kalaykov, Ivan
    Center for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden.
    Current Collector for Heavy Vehicles on Electrified Roads: Field Tests2016In: Journal of Asian Electric Vehicles, ISSN 1348-3927, Vol. 14, no 1, p. 1751-1757Article in journal (Refereed)
    Abstract [en]

    We present the field tests and measurements performed on a novel current collector manipulator to be mounted beneath a heavy vehicle to collect electric power from road embedded power lines. We describe the concept of the Electric Road System (ERS) test track being used and give an overview of the test vehicle for testing the current collection. The emphasis is on the field tests and measurements to evaluate both the vertical accelerations that the manipulator’s end-effector is subject to during operation and the performance of the detection and tracking of the power line.

    Download full text (pdf)
    fulltext
  • 31.
    Aldammad, Mohamad
    et al.
    Örebro University, School of Science and Technology.
    Ananiev, Anani
    Örebro University, School of Science and Technology.
    Kalaykov, Ivan
    Örebro University, School of Science and Technology.
    Current collector for heavy vehicles on electrified roads: kinematic analysis2014In: International journal of electric and hybrid vehicles, ISSN 1751-4088, E-ISSN 1751-4096, Vol. 6, no 4, p. 277-297Article in journal (Refereed)
    Abstract [en]

    We present a prototype of a novel current collector manipulator to be be mounted beneath a heavy vehicle to collect electric power from road-embedded power lines. We describe the concept of the ground-level power supply system for heavy vehicles and its main components. The main requirements and constraints, such as safety, robustness to harsh road and weather operational conditions, ambient environment aspects and dynamic properties, are introduced. The emphasis is on the developed kinematic model, which provides the base for further development of the control system. We propose and derive an alternative approach for representing the inverse kinematics by a two-dimensional polynomial approximation that avoids the usage of complicated non-linear equations. Its simplicity is demonstrated by a numerical example with the basic parameters of the prototype. The basic motion sequences of the current collector and the way to control them are outlined. 

  • 32.
    Aldammad, Mohamad
    et al.
    Örebro University, School of Science and Technology.
    Ananiev, Anani
    Örebro University, School of Science and Technology.
    Kalaykov, Ivan
    Örebro University, School of Science and Technology.
    Current Collector for Heavy Vehicles on Electrified Roads: Motion Control2015In: Journal of Asian Electric Vehicles, ISSN 1348-3927, Vol. 13, no 2, p. 1725-1732Article in journal (Refereed)
    Abstract [en]

    We present the adopted motion control schemes of a novel current collector manipulator to be mounted beneath a heavy hybrid electric vehicle to collect electric power from road embedded power lines. We describe our approach of power line detection and tracking based on an array of inductive proximity sensors. The emphasis is on the adopted motion control logic for sequential and closed loop motions to detect and track the power line respectively. We implement the sliding mode control approach for the closed loop control scheme as straightforward solution given the binary nature of the inductive proximity sensors being used. The overall architecture of the entire motion control system is presented. Finally, the implementation of the entire control logic in a form of a state machine is discussed.

    Download full text (pdf)
    fulltext
  • 33.
    Aldea, M.
    et al.
    Dpto. de Electrónica y Computadores, Universidad de Cantabria, Santander, Spain.
    Bernat, G.
    Department of Computer Science, University of York, United Kingdom.
    Broster, I.
    Department of Computer Science, University of York, United Kingdom.
    Burns, A.
    Department of Computer Science, University of York, United Kingdom.
    Dobrin, Radu
    Computer Engineering Department, Mälardalen University, Vasterås, Sweden.
    Drake, J. M.
    Dpto. de Electrónica y Computadores, Universidad de Cantabria, Santander, Spain.
    Fohlet, Gerhard
    Computer Engineering Department, Mälardalen University, Vasterås, Sweden.
    Gai, P.
    ReTiS Lab., Scuola Superiore Sant'Anna, Pisa, Italy.
    Harbour, M. G.
    Dpto. de Electrónica y Computadores, Universidad de Cantabria, Santander, Spain.
    Guidi, G.
    ReTiS Lab., Scuola Superiore Sant'Anna, Pisa, Italy.
    Gutierrez, J. J.
    Dpto. de Electrónica y Computadores, Universidad de Cantabria, Santander, Spain.
    Lennvall, Tomas
    Computer Engineering Department, Mälardalen University, Vasterås, Sweden.
    Lipari, G.
    ReTiS Lab., Scuola Superiore Sant'Anna, Pisa, Italy.
    Martinez, J. M.
    Dpto. de Electrónica y Computadores, Universidad de Cantabria, Santander, Spain.
    Medina, J. L.
    Dpto. de Electrónica y Computadores, Universidad de Cantabria, Santander, Spain.
    Palencia, J. C.
    Dpto. de Electrónica y Computadores, Universidad de Cantabria, Santander, Spain.
    Trimarchi, M.
    ReTiS Lab., Scuola Superiore Sant'Anna, Pisa, Italy.
    FSF: A real-time scheduling architecture framework2006In: 12th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'06): Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2006, p. 113-124Conference paper (Refereed)
    Abstract [en]

    Scheduling theory generally assumes that real-time systems are mostly composed of activities with hard real-time requirements. Many systems are built today by composing different applications or components in the same system, leading to a mixture of many different kinds of requirements with small parts of the system having hard real-time requirements and other larger parts with requirements for more flexible scheduling and for quality of service. Hard real-time scheduling techniques are extremely pessimistic for the latter part of the application, and consequently it is necessary to use techniques that let the system resources be fully utilized to achieve the highest possible quality. This paper presents a framework for a scheduling architecture that provides the ability to compose several applications or components into the system, and to flexibly schedule the available resources while guaranteeing hard real-time requirements. The framework (called FSF) is independent of the underlying implementation, and can run on different underlying scheduling strategies. It is based on establishing service contracts that represent the complex and flexible requirements of the applications, and which are managed by the underlying system to provide the required level of service.

  • 34.
    Aleotti, Jacopo
    et al.
    Örebro University, Department of Technology.
    Skoglund, Alexander
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    Position teaching of a robot arm by demonstration with a wearable input device2004Conference paper (Refereed)
    Abstract [en]

    This paper describes the first prototype of a "Programming by demonstration" (PbD) system for position teaching of a robot manipulator. A new approach for enabling PbD using supervised learning is presented, by connecting a wearable input device for sensing human arm movements to the software controller of a robot arm. The method does not require analytical modelling of either the human arm or robot, and can be customised for different users and robots. Initial experiments on some simple movements tasks are presented.

  • 35.
    Alfredsson, Anders
    et al.
    Örebro University, School of Science and Technology.
    Larsson, Gustav
    Örebro University, School of Science and Technology.
    Lokalisering och visualisering av område: En smartphone-applikation för en ökad trygghetskänsla2016Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The report is about different methods of localizing smartphones and the creation of an Android application. The application should visualize the Campus for Örebro university to raise awareness and the sense of security for people who are there at night. The

    implementation of the system is described along with the problems during development, and

    how they were solved.

    Download full text (pdf)
    fulltext
  • 36.
    Alhashimi, Anas
    et al.
    School of Science and Technology, Örebro University, Örebro, Sweden; Computer Engineering Department, University of Baghdad, Baghdad, Iraq.
    Adolfsson, Daniel
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    BFAR – Bounded False Alarm Rate detector for improved radar odometry estimation2021Conference paper (Refereed)
    Abstract [en]

    This paper presents a new detector for filtering noise from true detections in radar data, which improves the state of the art in radar odometry. Scanning Frequency-Modulated Continuous Wave (FMCW) radars can be useful for localisation and mapping in low visibility, but return a lot of noise compared to (more commonly used) lidar, which makes the detection task more challenging. Our Bounded False-Alarm Rate (BFAR) detector is different from the classical Constant False-Alarm Rate (CFAR) detector in that it applies an affine transformation on the estimated noise level after which the parameters that minimize the estimation error can be learned. BFAR is an optimized combination between CFAR and fixed-level thresholding. Only a single parameter needs to be learned from a training dataset. We apply BFAR tothe use case of radar odometry, and adapt a state-of-the-art odometry pipeline (CFEAR), replacing its original conservative filtering with BFAR. In this way we reduce the state-of-the-art translation/rotation odometry errors from 1.76%/0.5◦/100 m to 1.55%/0.46◦/100 m; an improvement of 12.5%.

  • 37.
    Alhashimi, Anas
    et al.
    Örebro University, School of Science and Technology. Computer Engineering Department, University of Baghdad, Baghdad, Iraq.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Knorn, Steffi
    Department of Autonomous Systems, Otto-von-Guericke University, Magdeburg, Germany..
    Varagnolo, Damiano
    Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway.
    Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 1, article id E155Article in journal (Refereed)
    Abstract [en]

    We consider the problem of calibrating range measurements of a Light Detection and Ranging (lidar) sensor that is dealing with the sensor nonlinearity and heteroskedastic, range-dependent, measurement error. We solved the calibration problem without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground. Then, its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. Moreover, we consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that "fixed features shall have fixed relative distances and angles". The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploy special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyze their statistical performance both in simulation and with field tests. We report the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observe that in field tests the approach can lead to a tenfold improvement in the accuracy of the raw measurements.

  • 38.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Hammar, Karl
    SICS - East Swedish ICT, Linköping, Sweden; Jönköping University, Jönköping, Sweden.
    Blomqvist, Eva
    SICS - East Swedish ICT, Linköping, Sweden.
    SmartEnv as a network of ontology patterns2018In: Semantic Web, ISSN 1570-0844, E-ISSN 2210-4968, Vol. 9, no 6, p. 903-918Article in journal (Refereed)
    Abstract [en]

    In this article we outline the details of an ontology, called SmartEnv, proposed as a representational model to assist the development process of smart (i.e., sensorized) environments. The SmartEnv ontology is described in terms of its modules representing different aspects including physical and conceptual aspects of a smart environment. We propose the use of the Ontology Design Pattern (ODP) paradigm in order to modularize our proposed solution, while at the same time avoiding strong dependencies between the modules in order to manage the representational complexity of the ontology. The ODP paradigm and related methodologies enable incremental construction of ontologies by first creating and then linking small modules. Most modules (patterns) of the SmartEnv ontology are inspired by, and aligned with, the Semantic Sensor Network (SSN) ontology, however with extra interlinks to provide further precision and cover more representational aspects.

    The result is a network of 8 ontology patterns together forming a generic representation for a smart environment. The patterns have been submitted to the ODP portal and are available on-line at stable URIs.

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    SmartEnv as a Network of Ontology Patterns
  • 39.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology. Örebro University, School of Law, Psychology and Social Work.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Exploiting Context and Semantics for UAV Path-finding in an Urban Setting2017In: Proceedings of the 1st International Workshop on Application of Semantic Web technologies in Robotics (AnSWeR 2017), Portoroz, Slovenia, May 29th, 2017 / [ed] Emanuele Bastianelli, Mathieu d'Aquin, Daniele Nardi, Technical University Aachen , 2017, p. 11-20Conference paper (Refereed)
    Abstract [en]

    In this paper we propose an ontology pattern that represents paths in a geo-representation model to be used in an aerial path planning processes. This pattern provides semantics related to constraints (i.e., ight forbidden zones) in a path planning problem in order to generate collision free paths. Our proposed approach has been applied on an ontology containing geo-regions extracted from satellite imagery data from a large urban city as an illustrative example.

    Download full text (pdf)
    Exploiting Context and Semantics for UAV Path-finding in an Urban Setting
  • 40.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Knowing without telling: integrating sensing and mapping for creating an artificial companion2016In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, New York, NY, USA: Association for Computing Machinery (ACM), 2016, p. 11:1-11:4Conference paper (Refereed)
    Abstract [en]

    This paper depicts a sensor-based map navigation approach which targets users, who due to disabilities or lack of technical knowledge are currently not in the focus of map system developments for personalized information. What differentiates our approach from the state-of-art mostly integrating localized social media data, is that our vision is to integrate real time sensor generated data that indicates the situation of dfferent phenomena (such as the physiological functions of the body) related to the user. The challenge hereby is mainly related to knowledge representation and integration. The tentative impact of our vision for future navigation systems is re ected within a scenario.

  • 41.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Automatic annotation of sensor data streams using abductive reasoning2013In: Proceedings of the International Conference on Knowledge Engineering and Ontology Development, SciTePress, 2013, p. 345-354Conference paper (Refereed)
  • 42.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Ontology alignment for classification of low level sensor data2012Conference paper (Refereed)
  • 43.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Reasoning for sensor data interpretation: an application to air quality monitoring2015In: Journal of Ambient Intelligence and Smart Environments, ISSN 1876-1364, E-ISSN 1876-1372, Vol. 7, no 4, p. 579-597Article in journal (Refereed)
    Abstract [en]

    In this paper we introduce a representation and reasoning model for the interpretation of time-series signals of a gas sensor situated in a sensor network. The interpretation process includes inferring high level explanations for changes detected over the gas signals. Inspired from the Semantic Sensor Network (SSN), the ontology used in this work provides an adaptive way of modelling the domain-related knowledge. Furthermore, exploiting (Incremental) Answer Set Programming (ASP) enables a declarative and automatic way of rule definition. Converting the ontology concepts and relations into ASP logic programs, the interpretation process defines a logic program whose answer sets are considered as eventual explanations for the detected changes in the gas sensor signals. The proposed approach is tested in a kitchen environment which contains several objects monitored by different sensors. The contextual information provided by the sensor network together with high level domain knowledge are used to infer explanations for changes in the ambient air detected by the gas sensors.

  • 44.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Towards Automatic Ontology Alignment for Enriching Sensor Data Analysis2013In: Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937, Vol. 415, p. 179-193Article in journal (Refereed)
    Abstract [en]

    In this work ontology alignment is used to align an ontology comprising high level knowledge to a structure representing the results of low-level sensor data classification. To resolve inherent uncertainties from the data driven classifier, an ontology about application domain is aligned to the classifier output and the result is recommendation system able to suggest a course of action that will resolve the uncertainty. This work is instantiated in a medical application domain where signals from an electronic nose are classified into different bacteria types. In case of misclassifications resulting from the data driven classifier, the alignment to an ontology representing traditional microbiology tests suggests a subset of tests most relevant to use. The result is a hybrid classification system (electronic nose and traditional testing) that automatically exploits domain knowledge in the identification process.

  • 45.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Open GeoSpatial Data as a Source of Ground Truth for Automated Labelling of Satellite Images2016In: SDW 2016: Spatial Data on the Web, Proceedings / [ed] Krzysztof Janowicz et al., CEUR Workshop Proceedings , 2016, p. 5-8Conference paper (Refereed)
  • 46.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Knowledge Representation and Reasoning Methods to Explain Errors in Machine Learning2020In: Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges / [ed] Ilaria Tiddi, Freddy Lécué, Pascal Hitzler, IOS Press, 2020Chapter in book (Refereed)
    Abstract [en]

    In this chapter we focus the use of knowledge representation and reasoning (KRR) methods as a guide to machine learning algorithms whereby relevant contextual knowledge can be leveraged upon. In this way, the learning methods improve performance by taking into account causal relationships behind errors. Performance improvement can be obtained by focusing the learning task on aspects that are particularly challenging (or prone to error), and then using added knowledge inferred by the reasoner as a means to provide further input to learning algorithms. Said differently, the KRR algorithms guide the learning algorithms, feeding it labels and data in order to iteratively reduce the errors calculated by a given cost function. This closed loop system comes with the added benefit that errors are also made more understandable to the human, as it is the task of the KRR system to contextualize the errors from the ML algorithm in accordance with its knowledge model. This represents a type of explainable AI that is focused on interpretability. This chapter will discuss the benefits of using KRR methods with ML methods in this way, and demonstrate an approach applied to satellite data for the purpose of improving classification and segmentation task.

  • 47.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Sioutis, Michael
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    A Symbolic Approach for Explaining Errors in Image Classification Tasks2018Conference paper (Refereed)
    Abstract [en]

    Machine learning algorithms, despite their increasing success in handling object recognition tasks, still seldom perform without error. Often the process of understanding why the algorithm has failed is the task of the human who, using domain knowledge and contextual information, can discover systematic shortcomings in either the data or the algorithm. This paper presents an approach where the process of reasoning about errors emerging from a machine learning framework is automated using symbolic techniques. By utilizing spatial and geometrical reasoning between objects in a scene, the system is able to describe misclassified regions in relation to its context. The system is demonstrated in the remote sensing domain where objects and entities are detected in satellite images.

  • 48.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Sioutis, Michael
    Department of Computer Science, Aalto University, Espoo, Finland.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Semantic Referee: A Neural-Symbolic Framework for Enhancing Geospatial Semantic Segmentation2019In: Semantic Web, ISSN 1570-0844, E-ISSN 2210-4968, Vol. 10, no 5, p. 863-880Article in journal (Refereed)
    Abstract [en]

    Understanding why machine learning algorithms may fail is usually the task of the human expert that uses domain knowledge and contextual information to discover systematic shortcomings in either the data or the algorithm. In this paper, we propose a semantic referee, which is able to extract qualitative features of the errors emerging from deep machine learning frameworks and suggest corrections. The semantic referee relies on ontological reasoning about spatial knowledge in order to characterize errors in terms of their spatial relations with the environment. Using semantics, the reasoner interacts with the learning algorithm as a supervisor. In this paper, the proposed method of the interaction between a neural network classifier and a semantic referee shows how to improve the performance of semantic segmentation for satellite imagery data.

  • 49.
    Alkeswani, Maria
    Örebro University, School of Science and Technology.
    Användargränssnitt för systematiskt experimenterandeCoordination_oru2023Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [sv]

    Coordination_oru är ett programramverk för forskning som skapades vid Örebrouniversitet i Sverige. Det är ett testramverk för en specifik algoritm för koordination avrobotar som utvecklas vidare till en simulationsplatform som möjliggör systematisktexperiment. I det här examensarbetet skapas en experimentspecifikation med alladelar som behövs för att fullt konfigurera systematiska experiment förCoordination_oru-ramverket. Experimentspecifikation utvecklas för att anpassa ettgrafiskt användargränssnitt som bidrar till att göra det enklare för användare attkontrollera, ändra och hantera systemet. Dessutom kan användare skapa och köraexperiment med möjlighet att justera karta, väg, robotens hastighet, acceleration,storlek/form, färg och destination samt att se resultatet. Användargränssnittet harutvecklats med JSON-formatet för att hantera konfiguration avexperimentspecifikation. Dessutom används CSV-formatet för att lagra resultatdata itabellform under projektet

    Download full text (pdf)
    fulltext
  • 50.
    Almeida, Tiago Rodrigues de
    et al.
    Örebro University, School of Science and Technology.
    Gutiérrez Maestro, Eduardo
    Örebro University, School of Science and Technology.
    Martinez Mozos, Oscar
    Örebro University, School of Science and Technology.
    Context-free Self-Conditioned GAN for Trajectory Forecasting2022In: 21st IEEE International Conference on Machine Learning and Applications. ICMLA 2022: Proceedings / [ed] Wani, MA; Kantardzic, M; Palade, V; Neagu, D; Yang, L; Chan, KY, IEEE, 2022, p. 1218-1223Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a context-free unsupervised approach based on a self-conditioned GAN to learn different modes from 2D trajectories. Our intuition is that each mode indicates a different behavioral moving pattern in the discriminator's feature space. We apply this approach to the problem of trajectory forecasting. We present three different training settings based on self-conditioned GAN, which produce better forecasters. We test our method in two data sets: human motion and road agents. Experimental results show that our approach outperforms previous context-free methods in the least representative supervised labels while performing well in the remaining labels. In addition, our approach outperforms globally in human motion, while performing well in road agents.

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