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  • 1.
    Adolfsson, Daniel
    et al.
    Örebro University, School of Science and Technology.
    Castellano-Quero, Manuel
    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.
    CorAl: Introspection for robust radar and lidar perception in diverse environments using differential entropy2022In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 155, article id 104136Article in journal (Refereed)
    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.

  • 2.
    Adolfsson, Daniel
    et al.
    Örebro University, School of Science and Technology.
    Karlsson, Mattias
    MRO Lab of the AASS Research Centre, Örebro University, Örebro, Sweden.
    Kubelka, Vladimír
    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.
    TBV Radar SLAM - Trust but Verify Loop Candidates2023In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 8, no 6, p. 3613-3620Article in journal (Refereed)
    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

    The full text will be freely available from 2025-06-01 00:00
  • 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.

    Download full text (pdf)
    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, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry2021In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), IEEE, 2021, p. 5462-5469Conference 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.

    Download full text (pdf)
    CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry
  • 5.
    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
  • 6.
    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. 

  • 7.
    Adolfsson, Daniel
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Liao, Qianfang
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    CorAl – Are the point clouds Correctly Aligned?2021In: 10th European Conference on Mobile Robots (ECMR 2021), IEEE, 2021, Vol. 10Conference 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.

    Download full text (pdf)
    CorAl – Are the point clouds Correctly Aligned?
  • 8.
    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%.

  • 9.
    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.

  • 10.
    Almeida, Tiago
    et al.
    Örebro University, School of Science and Technology.
    Rudenko, Andrey
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany.
    Schreiter, Tim
    Örebro University, School of Science and Technology.
    Zhu, Yufei
    Örebro University, School of Science and Technology.
    Gutiérrez Maestro, Eduardo
    Örebro University, School of Science and Technology.
    Morillo-Mendez, Lucas
    Örebro University, School of Science and Technology.
    Kucner, Tomasz P.
    Mobile Robotics Group, Department of Electrical Engineering and Automation, Aalto University, Finland; FCAI, Finnish Center for Artificial Intelligence, Finland.
    Martinez Mozos, Oscar
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Palmieri, Luigi
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany.
    Arras, Kai O.
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    THÖR-Magni: Comparative Analysis of Deep Learning Models for Role-Conditioned Human Motion Prediction2023In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, p. 2200-2209Conference paper (Refereed)
    Abstract [en]

    Autonomous systems, that need to operate in human environments and interact with the users, rely on understanding and anticipating human activity and motion. Among the many factors which influence human motion, semantic attributes, such as the roles and ongoing activities of the detected people, provide a powerful cue on their future motion, actions, and intentions. In this work we adapt several popular deep learning models for trajectory prediction with labels corresponding to the roles of the people. To this end we use the novel THOR-Magni dataset, which captures human activity in industrial settings and includes the relevant semantic labels for people who navigate complex environments, interact with objects and robots, work alone and in groups. In qualitative and quantitative experiments we show that the role-conditioned LSTM, Transformer, GAN and VAE methods can effectively incorporate the semantic categories, better capture the underlying input distribution and therefore produce more accurate motion predictions in terms of Top-K ADE/FDE and log-likelihood metrics.

  • 11.
    Almqvist, Håkan
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Learning to detect misaligned point clouds2018In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 35, no 5, p. 662-677Article in journal (Refereed)
    Abstract [en]

    Matching and merging overlapping point clouds is a common procedure in many applications, including mobile robotics, three-dimensional mapping, and object visualization. However, fully automatic point-cloud matching, without manual verification, is still not possible because no matching algorithms exist today that can provide any certain methods for detecting misaligned point clouds. In this article, we make a comparative evaluation of geometric consistency methods for classifying aligned and nonaligned point-cloud pairs. We also propose a method that combines the results of the evaluated methods to further improve the classification of the point clouds. We compare a range of methods on two data sets from different environments related to mobile robotics and mapping. The results show that methods based on a Normal Distributions Transform representation of the point clouds perform best under the circumstances presented herein.

  • 12.
    Almqvist, Håkan
    et al.
    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.
    Improving Point Cloud Accuracy Obtained from a Moving Platform for Consistent Pile Attack Pose Estimation2014In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 75, no 1, p. 101-128Article in journal (Refereed)
    Abstract [en]

    We present a perception system for enabling automated loading with waist-articulated wheel loaders. To enable autonomous loading of piled materials, using either above-ground wheel loaders or underground load-haul-dump vehicles, 3D data of the pile shape is needed. However, using common 3D scanners, the scan data is distorted while the wheel loader is moving towards the pile. Existing methods that make use of 3D scan data (for autonomous loading as well as tasks such as mapping, localisation, and object detection) typically assume that each 3D scan is accurate. For autonomous robots moving over rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one 3D scan, in which case the scan data will be distorted. We present a study of auto-loading methods, and how to locate piles in real-world scenarios with nontrivial ground geometry. We have compared how consistently each method performs for live scans acquired in motion, and also how the methods perform with different view points and scan configurations. The system described in this paper uses a novel method for improving the quality of distorted 3D scans made from a vehicle moving over uneven terrain. The proposed method for improving scan quality is capable of increasing the accuracy of point clouds without assuming any specific features of the environment (such as planar walls), without resorting to a “stop-scan-go” approach, and without relying on specialised and expensive hardware. Each new 3D scan is registered to the preceding using the normal-distributions transform (NDT). After each registration, a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. To verify the impact of the quality improvement, we present data that shows how auto-loading methods benefit from the corrected scans. The presented methods are validated on data from an autonomous wheel loader, as well as with simulated data. The proposed scan-correction method increases the accuracy of both the vehicle trajectory and the point cloud. We also show that it increases the reliability of pile-shape measures used to plan an efficient attack pose when performing autonomous loading.

  • 13.
    Almqvist, Håkan
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Improving Point-Cloud Accuracy from a Moving Platform in Field Operations2013In: 2013 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2013, p. 733-738Conference paper (Refereed)
    Abstract [en]

    This paper presents a method for improving the quality of distorted 3D point clouds made from a vehicle equipped with a laser scanner moving over uneven terrain. Existing methods that use 3D point-cloud data (for tasks such as mapping, localisation, and object detection) typically assume that each point cloud is accurate. For autonomous robots moving in rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one point cloud, in which case the data will be distorted. The method proposed in this paper is capable of increasing the accuracy of 3D point clouds, without assuming any specific features of the environment (such as planar walls), without resorting to a "stop-scan-go" approach, and without relying on specialised and expensive hardware. Each new point cloud is matched to the previous using normal-distribution-transform (NDT) registration, after which a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. The proposed method increases the accuracy of both the measured platform trajectory and the point cloud. The method is validated on both real-world and simulated data.

  • 14.
    Amigoni, Francesco
    et al.
    Politecnico di Milano, Milan, Italy.
    Yu, Wonpil
    Electronics and Telecommunications Research Institute (ETRI), Daejeon, South Korea.
    Andre, Torsten
    University of Klagenfurt, Klagenfurt, Austria.
    Holz, Dirk
    University of Bonn, Bonn, Germany.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Matteucci, Matteo
    Politecnico di Milano, Milan, Italy.
    Moon, Hyungpil
    Sungkyunkwan University, Suwon, South Korea.
    Yokozuka, Masashi
    Nat. Inst. of Advanced Industrial Science and Technology, Tsukuba, Japan.
    Biggs, Geoffrey
    Nat. Inst. of Advanced Industrial Science and Technology, Tsukuba, Japan.
    Madhavan, Raj
    Amrita University, Clarksburg MD, United States of America.
    A Standard for Map Data Representation: IEEE 1873-2015 Facilitates Interoperability Between Robots2018In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 25, no 1, p. 65-76Article in journal (Refereed)
    Abstract [en]

    The availability of environment maps for autonomous robots enables them to complete several tasks. A new IEEE standard, IEEE 1873-2015, Robot Map Data Representation for Navigation (MDR) [15], sponsored by the IEEE Robotics and Automation Society (RAS) and approved by the IEEE Standards Association Standards Board in September 2015, defines a common representation for two-dimensional (2-D) robot maps and is intended to facilitate interoperability among navigating robots. The standard defines an extensible markup language (XML) data format for exchanging maps between different systems. This article illustrates how metric maps, topological maps, and their combinations can be represented according to the standard.

    Download full text (pdf)
    IEEE 1873-2015: A Standard for Map Data Representation
  • 15.
    Andreasson, Henrik
    et al.
    Örebro University, School of Science and Technology.
    Adolfsson, Daniel
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Incorporating Ego-motion Uncertainty Estimates in Range Data Registration2017In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1389-1395Conference 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.

  • 16.
    Andreasson, Henrik
    et al.
    Örebro University, Department of Technology.
    Magnusson, Martin
    Örebro University, Department of Technology.
    Lilienthal, Achim
    Örebro University, Department of Natural Sciences.
    Has something changed here?: Autonomous difference detection for security patrol robots2007In: 2007 IEEE/RSJ international conference on intelligent robots and systems, New York, NY, USA: IEEE, 2007, p. 3429-3435, article id 4399381Conference paper (Refereed)
    Abstract [en]

    This paper presents a system for autonomous change detection with a security patrol robot. In an initial step a reference model of the environment is created and changes are then detected with respect to the reference model as differences in coloured 3D point clouds, which are obtained from a 3D laser range scanner and a CCD camera. The suggested approach introduces several novel aspects, including a registration method that utilizes local visual features to determine point correspondences (thus essentially working without an initial pose estimate) and the 3D-NDT representation with adaptive cell size to efficiently represent both the spatial and colour aspects of the reference model. Apart from a detailed description of the individual parts of the difference detection system, a qualitative experimental evaluation in an indoor lab environment is presented, which demonstrates that the suggested system is able register and detect changes in spatial 3D data and also to detect changes that occur in colour space and are not observable using range values only.

    Download full text (pdf)
    Has Something Changed Here?: Autonomous Difference Detection for Security Patrol Robots
  • 17.
    Brandão, Martim
    et al.
    Department of Informatics, King's College London, London, United Kingdom.
    Mansouri, Masoumeh
    School of Computer Science, University of Birmingham, Birmingham, United Kingdom.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Editorial: Responsible Robotics2022In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 9, article id 937612Article in journal (Refereed)
  • 18.
    Fan, Hongqi
    et al.
    Örebro University, School of Science and Technology. National Laboratory of Science and Technology on Automatic Target Recognition, National University of Defense Technology, Changsha, China.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Li, Tiancheng
    School of Sciences, University of Salamanca, Salamanca, Spain.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment2018In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 9, p. 2977-2993Article in journal (Refereed)
    Abstract [en]

    Environment monitoring remains a major challenge for mobile robots, especially in densely cluttered or highly populated dynamic environments, where uncertainties originated from environment and sensor significantly challenge the robot's perception. This paper proposes an effective occupancy filtering method called the dual probability hypothesis density (DPHD) filter, which models uncertain phenomena, such as births, deaths, occlusions, false alarms, and miss detections, by using random finite sets. The key insight of our method lies in the connection of the idea of dynamic occupancy with the concepts of the phase space density in gas kinetic and the PHD in multiple target tracking. By modeling the environment as a mixture of static and dynamic parts, the DPHD filter separates the dynamic part from the static one with a unified filtering process, but has a higher computational efficiency than existing Bayesian Occupancy Filters (BOFs). Moreover, an adaptive newborn function and a detection model considering occlusions are proposed to improve the filtering efficiency further. Finally, a hybrid particle implementation of the DPHD filter is proposed, which uses a box particle filter with constant discrete states and an ordinary particle filter with a time-varying number of particles in a continuous state space to process the static part and the dynamic part, respectively. This filter has a linear complexity with respect to the number of grid cells occupied by dynamic obstacles. Real-world experiments on data collected by a lidar at a busy roundabout demonstrate that our approach can handle monitoring of a highly dynamic environment in real time.

  • 19.
    Fan, Hongqi
    et al.
    National University of Defense Technology, Changsa, P. R. China.
    Lu, Dawei
    National University of Defense Technology, Changsa, P. R. China.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    2D Spatial Keystone Transform for Sub-Pixel Motion Extraction from Noisy Occupancy Grid Map2018In: Proceedings of 21st International Conference on Information Fusion (FUSION), 2018, p. 2400-2406Conference paper (Refereed)
    Abstract [en]

    In this paper, we propose a novel sub-pixel motion extraction method, called as Two Dimensional Spatial Keystone Transform (2DS-KST), for the motion detection and estimation from successive noisy Occupancy Grid Maps (OGMs). It extends the KST in radar imaging or motion compensation to 2D real spatial case, based on multiple hypotheses about possible directions of moving obstacles. Simulation results show that 2DS-KST has a good performance on the extraction of sub-pixel motions in very noisy environment, especially for those slowly moving obstacles.

    Download full text (pdf)
    2D spatial keystone transform for sub-pixel motion extraction from noisy occupancy grid map
  • 20.
    Gabellieri, Chiara
    et al.
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Palleschi, Alessandro
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Mannucci, Anna
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Pierallini, Michele
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Stefanini, Elisa
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Catalano, Manuel G.
    Istituto Italiano di Tecnologia, Genova GE, Italy.
    Caporale, Danilo
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Settimi, Alessandro
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Garabini, Manolo
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Pallottino, Lucia
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Towards an Autonomous Unwrapping System for Intralogistics2019In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 4, no 4, p. 4603-4610Article in journal (Refereed)
    Abstract [en]

    Warehouse logistics is a rapidly growing market for robots. However, one key procedure that has not received much attention is the unwrapping of pallets to prepare them for objects picking. In fact, to prevent the goods from falling and to protect them, pallets are normally wrapped in plastic when they enter the warehouse. Currently, unwrapping is mainly performed by human operators, due to the complexity of its planning and control phases. Autonomous solutions exist, but usually they are designed for specific situations, require a large footprint and are characterized by low flexibility. In this work, we propose a novel integrated robotic solution for autonomous plastic film removal relying on an impedance-controlled robot. The main contribution is twofold: on one side, a strategy to plan Cartesian impedance and trajectory to execute the cut without damaging the goods is discussed; on the other side, we present a cutting device that we designed for this purpose. The proposed solution presents the characteristics of high versatility and the need for a reduced footprint, due to the adopted technologies and the integration with a mobile base. Experimental results are shown to validate the proposed approach.

    Download full text (pdf)
    Towards an Autonomous Unwrapping System for Intralogistics
  • 21.
    Gholami Shahbandi, Saeed
    et al.
    Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    2D map alignment with region decomposition2019In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 43, no 5, p. 1117-1136Article in journal (Refereed)
    Abstract [en]

    In many applications of autonomous mobile robots the following problem is encountered. Two maps of the same environment are available, one a prior map and the other a sensor map built by the robot. To benefit from all available information in both maps, the robot must find the correct alignment between the two maps. There exist many approaches to address this challenge, however, most of the previous methods rely on assumptions such as similar modalities of the maps, same scale, or existence of an initial guess for the alignment. In this work we propose a decomposition-based method for 2D spatial map alignment which does not rely on those assumptions. Our proposed method is validated and compared with other approaches, including generic data association approaches and map alignment algorithms. Real world examples of four different environments with thirty six sensor maps and four layout maps are used for this analysis. The maps, along with an implementation of the method, are made publicly available online.

  • 22.
    Gholami Shahbandi, Saeed
    et al.
    Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Iagnemma, Karl
    Robotic Mobility Group, Massachusetts Institute of Technology, Cambridge MA, USA.
    Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer2018In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 3, no 3, p. 2040-2047Article in journal (Refereed)
    Abstract [en]

    We propose a method based on a nonlinear transformation for nonrigid alignment of maps of different modalities, exemplified with matching partial and deformed two-dimensional maps to layout maps. For two types of indoor environments, over a dataset of 40 maps, we have compared the method to state-of-the-art map matching and nonrigid image registration methods and demonstrate a success rate of 80.41% and a mean point-to-point alignment error of 1.78 m, compared to 31.9% and 10.7 m for the best alternative method. We also propose a fitness measure that can quite reliably detect bad alignments. Finally, we show a use case of transferring prior knowledge (labels/segmentation), demonstrating that map segmentation is more consistent when transferred from an aligned layout map than when operating directly on partial maps (95.97% vs. 81.56%).

    Download full text (pdf)
    Nonlinear Optimization of Multimodal 2D Map Alignment with Application to Prior Knowledge Transfer
  • 23.
    Gupta, Himanshu
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Julier, Simon
    Department of Computer Science, University College London, London, England.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology. Perception for Intelligent Systems, Technical University of Munich, Germany .
    Revisiting Distribution-Based Registration Methods2023In: 2023 European Conference on Mobile Robots (ECMR) / [ed] Marques, L.; Markovic, I., IEEE , 2023, p. 43-48Conference paper (Refereed)
    Abstract [en]

    Normal Distribution Transformation (NDT) registration is a fast, learning-free point cloud registration algorithm that works well in diverse environments. It uses the compact NDT representation to represent point clouds or maps as a spatial probability function that models the occupancy likelihood in an environment. However, because of the grid discretization in NDT maps, the global minima of the registration cost function do not always correlate to ground truth, particularly for rotational alignment. In this study, we examined the NDT registration cost function in-depth. We evaluated three modifications (Student-t likelihood function, inflated covariance/heavily broadened likelihood curve, and overlapping grid cells) that aim to reduce the negative impact of discretization in classical NDT registration. The first NDT modification improves likelihood estimates for matching the distributions of small population sizes; the second modification reduces discretization artifacts by broadening the likelihood tails through covariance inflation; and the third modification achieves continuity by creating the NDT representations with overlapping grid cells (without increasing the total number of cells). We used the Pomerleau Dataset evaluation protocol for our experiments and found significant improvements compared to the classic NDT D2D registration approach (27.7% success rate) using the registration cost functions "heavily broadened likelihood NDT" (HBL-NDT) (34.7% success rate) and "overlapping grid cells NDT" (OGC-NDT) (33.5% success rate). However, we could not observe a consistent improvement using the Student-t likelihood-based registration cost function (22.2% success rate) over the NDT P2D registration cost function (23.7% success rate). A comparative analysis with other state-of-art registration algorithms is also presented in this work. We found that HBL-NDT worked best for easy initial pose difficulties scenarios making it suitable for consecutive point cloud registration in SLAM application.

  • 24.
    Huhle, Benjamin
    et al.
    Department of Graphical Interactive Systems WSI/GRIS, University of Tübingen, Germany.
    Magnusson, Martin
    Örebro University, Department of Technology.
    Straßer, Wolfgang
    Department of Graphical Interactive Systems WSI/GRIS, University of Tübingen, Germany.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Registration of colored 3D point clouds with a Kernel-based extension to the normal distributions transform2008In: 2008 IEEE international conference on robotics and automation, New York, NY, USA: IEEE, 2008, p. 4025-4030, article id 4543829Conference paper (Refereed)
    Abstract [en]

    We present a new algorithm for scan registration of colored 3D point data which is an extension to the Normal Distributions Transform (NDT). The probabilistic approach of NDT is extended to a color-aware registration algorithm by modeling the point distributions as Gaussian mixture-models in color space. We discuss different point cloud registration techniques, as well as alternative variants of the proposed algorithm. Results showing improved robustness of the proposed method using real-world data acquired with a mobile robot and a time-of-flight camera are presented.

    Download full text (pdf)
    Registration of Colored 3D Point Clouds with a Kernel-based Extension to the Normal Distributions Transform
  • 25.
    Kucner, Tomasz
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Tell me about dynamics!: Mapping velocity fields from sparse samples with Semi-Wrapped Gaussian Mixture Models2016In: Robotics: Science and Systems Conference (RSS 2016), 2016Conference paper (Refereed)
    Abstract [en]

    Autonomous mobile robots often require informa-tion about the environment beyond merely the shape of thework-space. In this work we present a probabilistic method formappingdynamics, in the sense of learning and representingstatistics about the flow of discrete objects (e.g., vehicles, people)as well as continuous media (e.g., air flow). We also demonstratethe capabilities of the proposed method with two use cases. Onerelates to motion planning in populated environments, whereinformation about the flow of people can help robots to followsocial norms and to learn implicit traffic rules by observingthe movements of other agents. The second use case relates toMobile Robot Olfaction (MRO), where information about windflow is crucial for most tasks, including e.g. gas detection, gasdistribution mapping and gas source localisation. We representthe underlying velocity field as a set of Semi-Wrapped GaussianMixture Models (SWGMM) representing the learnt local PDF ofvelocities. To estimate the parameters of the PDF we employ aformulation of Expectation Maximisation (EM) algorithm specificfor SWGMM. We also describe a data augmentation methodwhich allows to build a dense dynamic map based on a sparseset of measurements. In case only a small set of observations isavailable we employ a hierarchical sampling method to generatevirtual observations from existing mixtures.

    Download full text (pdf)
    fulltext
  • 26.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro University, School of Science and Technology.
    Closing Remarks2020In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 143-151Chapter in book (Refereed)
    Abstract [en]

    Dynamics is an inherent feature of reality. In spite of that, the domain of maps of dynamics has not received a lot of attention yet. In this book, we present solutions for building maps of dynamics and outline how to make use of them for motion planning. In this chapter, we present discuss related research question that as of yet remain to be answered, and derive possible future research directions. 

  • 27.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany .
    Swaminathan, Chittaranjan Srinivas
    Örebro University, School of Science and Technology.
    Introduction2020In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 1-13Chapter in book (Refereed)
    Abstract [en]

    Change and motion are inherent features of reality. The ability to recognise patterns governing changes has allowed humans to thrive in a dynamic reality. Similarly, dynamics awareness can also improve the performance of robots. Dynamics awareness is an umbrella term covering a broad spectrum of concepts. In this chapter, we present the key aspects of dynamics awareness. We introduce two motivating examples presenting the challenges for robots operating in a dynamic environment. We discuss the benefits of using spatial models of dynamics and analyse the challenges of building such models.

  • 28.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro University, School of Science and Technology.
    Maps of Dynamics2020In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 15-32Chapter in book (Refereed)
    Abstract [en]

    The task of building maps of dynamics is the key focus of this book, as well as how to use them for motion planning. In this chapter, we present a categorisation and overview of different types of maps of dynamics. Furthermore, we give an overview of approaches to motion planning in dynamic environments, with a focus on motion planning over maps of dynamics. 

  • 29.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro University, School of Science and Technology.
    Modelling Motion Patterns with Circular-Linear Flow Field Maps2020In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 65-113Chapter in book (Refereed)
    Abstract [en]

    The shared feature of the flow of discrete objects and continuous media is that they both can be represented as velocity vectors encapsulating direction and speed of motion. In this chapter, we present a method for modelling the flow of discrete objects and continuous media as continuous Gaussian mixture fields. The proposed model associates to each part of the environment a Gaussian mixture model describing the local motion patterns. We also present a learning method, designed to build the model from a set of sparse, noisy and incomplete observations. 

  • 30.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro University, School of Science and Technology.
    Modelling Motion Patterns with Conditional Transition Map2020In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 33-64Chapter in book (Refereed)
    Abstract [en]

    The key idea of modelling flow of discrete objects is to capture the way they move through the environment. One method to capture the flow is to observe changes in occupancy caused by the motion of discrete objects. In this chapter, we present a method to model and learn occupancy shifts caused by an object moving through the environment. The key idea is observe temporal changes changes in the occupancy of adjacent cells, and based on the temporal offset infer the direction of the occupancy flow.

  • 31.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro University, School of Science and Technology.
    Motion Planning Using MoDs2020In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 115-141Chapter in book (Refereed)
    Abstract [en]

    Maps of dynamics can be beneficial for motion planning. Information about motion patterns in the environment can lead to finding flow-aware paths, allowing robots to align better to the expected motion: either of other agents in the environment or the flow of air or another medium. The key idea of flow-aware motion planning is to include adherence to the flow represented in the MoD into the motion planning algorithm’s sub-units (i.e. cost function, sampling mechanism), thereby biasing the motion planner into obeying local and implicit traffic rules. 

  • 32.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Palmieri, Luigi
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro University, School of Science and Technology.
    Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots2020Book (Other academic)
    Abstract [en]

    This book describes how robots can make sense of motion in their surroundings and use the patterns they observe to blend in better in dynamic environments shared with humans.The world around us is constantly changing. Nonetheless, we can find our way and aren’t overwhelmed by all the buzz, since motion often follows discernible patterns. Just like humans, robots need to understand the patterns behind the dynamics in their surroundings to be able to efficiently operate e.g. in a busy airport. Yet robotic mapping has traditionally been based on the static world assumption, which disregards motion altogether. In this book, the authors describe how robots can instead explicitly learn patterns of dynamic change from observations, store those patterns in Maps of Dynamics (MoDs), and use MoDs to plan less intrusive, safer and more efficient paths. The authors discuss the pros and cons of recently introduced MoDs and approaches to MoD-informed motion planning, and provide an outlook on future work in this emerging, fascinating field. 

  • 33.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Luperto, Matteo
    Applied Intelligent System Lab (AISLab), Università degli Studi di Milano, Milano, Italy.
    Lowry, Stephanie
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Robust Frequency-Based Structure Extraction2021In: 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2021, p. 1715-1721Conference paper (Refereed)
    Abstract [en]

    State of the art mapping algorithms can produce high-quality maps. However, they are still vulnerable to clutter and outliers which can affect map quality and in consequence hinder the performance of a robot, and further map processing for semantic understanding of the environment. This paper presents ROSE, a method for building-level structure detection in robotic maps. ROSE exploits the fact that indoor environments usually contain walls and straight-line elements along a limited set of orientations. Therefore metric maps often have a set of dominant directions. ROSE extracts these directions and uses this information to segment the map into structure and clutter through filtering the map in the frequency domain (an approach substantially underutilised in the mapping applications). Removing the clutter in this way makes wall detection (e.g. using the Hough transform) more robust. Our experiments demonstrate that (1) the application of ROSE for decluttering can substantially improve structural feature retrieval (e.g., walls) in cluttered environments, (2) ROSE can successfully distinguish between clutter and structure in the map even with substantial amount of noise and (3) ROSE can numerically assess the amount of structure in the map.

    Download full text (pdf)
    Robust Frequency-Based Structure Extraction
  • 34.
    Kucner, Tomasz Piotr
    et al.
    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.
    Where am I?: An NDT-based prior for MCL2015In: 2015 European Conference on Mobile Robots (ECMR), New York: IEEE conference proceedings , 2015Conference paper (Refereed)
    Abstract [en]

    One of the key requirements of autonomous mobile robots is a robust and accurate localisation system. Recent advances in the development of Monte Carlo Localisation (MCL) algorithms, especially the Normal Distribution Transform Monte Carlo Localisation (NDT-MCL), provides memory-efficient reliable localisation with industry-grade precision. We propose an approach for building an informed prior for NDT-MCL (in fact for any MCL algorithm) using an initial observation of the environment and its map. Leveraging on the NDT map representation, we build a set of poses using partial observations. After that we construct a Gaussian Mixture Model (GMM) over it. Next we obtain scores for each distribution in GMM. In this way we obtain in an efficient way a prior for NDT-MCL. Our approach provides a more focused then uniform initial distribution, concentrated in states where the robot is more likely to be, by building a Gaussian mixture model over potential poses. We present evaluations and quantitative results using real-world data from an indoor environment. Our experiments show that, compared to a uniform prior, the proposed method significantly increases the number of successful initialisations of NDT-MCL and reduces the time until convergence, at a negligible initial cost for computing the prior.

  • 35.
    Kucner, Tomasz Piotr
    et al.
    Mobile Robotics Group, School of Electrical Engineering, Aalto University, Finland; Finnish Center for Artificial Intelligence, Finland.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Mghames, Sariah
    L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK.
    Palmieri, Luigi
    BOSCH Corporate Research, Renningen, Germany.
    Verdoja, Francesco
    Intelligent Robotics Group, School of Electrical Engineering, Aalto University, Finland.
    Swaminathan, Chittaranjan Srinivas
    Örebro University, School of Science and Technology.
    Krajnik, Tomas
    Artificial Intelligence Center, Czech Technical University, Praha, Czechia.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Bellotto, Nicola
    L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK; Department of Information Engineering, Univeristy of Padua, Padova, Italy.
    Hanheide, Marc
    L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology. Technical Univeristy of Munich, Munich, Germany.
    Survey of maps of dynamics for mobile robots2023In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 42, no 11, p. 977-1006Article in journal (Refereed)
    Abstract [en]

    Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

  • 36.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor Manuel
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Enabling Flow Awareness for Mobile Robots in Partially Observable Environments2017In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 2, no 2, p. 1093-1100Article in journal (Refereed)
    Abstract [en]

    Understanding the environment is a key requirement for any autonomous robot operation. There is extensive research on mapping geometric structure and perceiving objects. However, the environment is also defined by the movement patterns in it. Information about human motion patterns can, e.g., lead to safer and socially more acceptable robot trajectories. Airflow pattern information allow to plan energy efficient paths for flying robots and improve gas distribution mapping. However, modelling the motion of objects (e.g., people) and flow of continuous media (e.g., air) is a challenging task. We present a probabilistic approach for general flow mapping, which can readily handle both of these examples. Moreover, we present and compare two data imputation methods allowing to build dense maps from sparsely distributed measurements. The methods are evaluated using two different data sets: one with pedestrian data and one with wind measurements. Our results show that it is possible to accurately represent multimodal, turbulent flow using a set of Gaussian Mixture Models, and also to reconstruct a dense representation based on sparsely distributed locations.

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    Enabling Flow Awareness for Mobile Robots in Partially Observable Environments
  • 37.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Swaminathan, Chittaranjan Srinivas
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Palmieri, L.
    Corporate Research Robert Bosch GmbH, Renningen, Germany.
    Preface2020In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. vii-xChapter in book (Refereed)
  • 38.
    Kucner, Tomasz
    et al.
    Örebro University, School of Science and Technology.
    Sarinen, Jari
    Aalto university, Helsinki, Finland.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Conditional transition maps: learning motion patterns in dynamic environments2013In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2013, p. 1196-1201Conference paper (Refereed)
    Abstract [en]

    In this paper we introduce a method for learning motion patterns in dynamic environments. Representations of dynamic environments have recently received an increasing amount of attention in the research community. Understanding dynamic environments is seen as one of the key challenges in order to enable autonomous navigation in real-world scenarios. However, representing the temporal dimension is a challenge yet to be solved. In this paper we introduce a spatial representation, which encapsulates the statistical dynamic behavior observed in the environment. The proposed Conditional Transition Map (CTMap) is a grid-based representation that associates a probability distribution for an object exiting the cell, given its entry direction. The transition parameters are learned from a temporal signal of occupancy on cells by using a local-neighborhood cross-correlation method. In this paper, we introduce the CTMap, the learning approach and present a proof-of-concept method for estimating future paths of dynamic objects, called Conditional Probability Propagation Tree (CPPTree). The evaluation is done using a real-world data-set collected at a busy roundabout.

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    ctmap.pdf
  • 39.
    Luperto, Matteo
    et al.
    University of Milan, Milan, Italy.
    Kucner, Tomasz Piotr
    Örebro University, Örebro, Sweden; Aalto University, Espoo, Finland .
    Tassi, Andrea
    Politecnico di Milano, Milan, Italy.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Amigoni, Francesco
    Politecnico di Milano, Milan, Italy.
    Robust Structure Identification and Room Segmentation of Cluttered Indoor Environments From Occupancy Grid Maps2022In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, no 3, p. 7974-7981Article in journal (Refereed)
    Abstract [en]

    Identifying the environment's structure, through detecting core components such as rooms and walls, can facilitate several tasks fundamental for the successful operation of indoor autonomous mobile robots, including semantic environment understanding. These robots often rely on 2D occupancy maps for core tasks such as localisation and motion and task planning. However, reliable identification of structure and room segmentation from 2D occupancy maps is still an open problem due to clutter (e.g., furniture and movable objects), occlusions, and partial coverage. We propose a method for the RObust StructurE identification and ROom SEgmentation (ROSE2) of 2D occupancy maps thatmay be cluttered and incomplete. ROSE2 identifies the main directions of walls and is resilient to clutter and partial observations, allowing to extract a clean, abstract geometrical floor-plan-like description of the environment, which is used to segment, i.e., to identify rooms in, the original occupancy grid map. ROSE2 is tested in several real-world publicly available cluttered maps obtained in different conditions. The results show that it can robustly identify the environment structure in 2D occupancy maps suffering fromclutter and partial observations, while significantly improving room segmentation accuracy. Thanks to the combination of clutter removal and robust room segmentation, ROSE2 consistently achieves higher performance than the state-of-the-art methods, against which it is compared.

  • 40.
    Magnusson, Martin
    Örebro University, Department of Technology. AASS.
    3D Scan Matching for Mobile Robots with Application to Mine Mapping: Licentiate Thesis2006Licentiate thesis, monograph (Other academic)
    Abstract [en]

    This thesis is concerned with three-dimensional scan registration, in particular of underground mine tunnels. Registration of partial range scans is an essential part of building 3D maps, and autonomous creation of reliable maps is a first step towards autonomous mining vehicles. The thesis presents a survey of relevant sensor technology and discusses the advantages and disadvantages of different sensors for use in mine environments. A survey of the state of the art in scan registration algorithms is also presented, as well as a number of relevant applications. A new algorithm for registration of 3D data is presented, which is a generalisation of the normal distributions transform (NDT) for 2D data developed by Biber and Straßer. A detailed quantitative and qualitative comparison of the new algorithm with existing registration algorithms is shown. Results with actual mine data, some of which were collected with a new prototype 3D laser scanner, show that the presented algorithm is faster and in many cases more accurate, compared with the current standard in 3D registration.

  • 41.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    The three-dimensional normal-distributions transform: an efficient representation for registration, surface analysis, and loop detection2009Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This dissertation is concerned with three-dimensional (3D) sensing and 3D scan representation. Three-dimensional records are important tools in several disciplines; such as medical imaging, archaeology, and mobile robotics. This dissertation proposes the normal-distributions transform, NDT, as a general 3D surface representation with applications in scan registration, localisation, loop detection, and surface-structure analysis. After applying NDT, the surface is represented by a smooth function with analytic derivatives. This representation has several attractive properties.

    The smooth function representation makes it possible to use standard numerical optimisation methods, such as Newton’s method, for 3D registration. This dissertation extends the original two-dimensional NDT registration algorithm of Biber and Straßer to 3D and introduces a number of improvements. The 3D-NDT scan-registration algorithm is compared to current de facto standard registration algorithms. 3D-NDT scan registration with the proposed extensions is shown to be more robust, more accurate, and faster than the popular ICP algorithm. An additional benefit is that 3D-NDT registration provides a confidence measure of the result with little additional effort.

    Furthermore, a kernel-based extension to 3D-NDT for registering coloured data is proposed. Approaches based on local visual features typically use only a small fraction of the available 3D points for registration. In contrast, Colour-NDT uses all of the available 3D data. The dissertation proposes to use a combination of local visual features and Colour-NDT for robust registration of coloured 3D scans.

    Also building on NDT, a novel approach using 3D laser scans to perform appearance-based loop detection for mobile robots is proposed. Loop detection is an importantproblem in the SLAM (simultaneous localisation and mapping) domain. The proposed approach uses only the appearance of 3D point clouds to detect loops and requires nopose information. It exploits the NDT surface representation to create histograms based on local surface orientation and smoothness. The surface-shape histograms compress the input data by two to three orders of magnitude. Because of the high compression rate, the histograms can be matched efficiently to compare the appearance of two scans. Rotation invariance is achieved by aligning scans with respect to dominant surface orientations. In order to automatically determine the threshold that separates scans at loop closures from nonoverlapping ones, the proposed approach uses expectation maximisation to fit a Gamma mixture model to the output similarity measures.

    In order to enable more high-level tasks, it is desirable to extract semantic information from 3D models. One important task where such 3D surface analysis is useful is boulder detection for mining vehicles. This dissertation presents a method, also inspired by NDT, that provides clues as to where the pile is, where the bucket should be placed for loading, and where there are obstacles. The points of 3D point clouds are classified based on the surrounding surface roughness and orientation. Other potential applications include extraction of drivable paths over uneven surfaces.

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  • 42.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Almqvist, Håkan
    Örebro University, School of Science and Technology.
    Consistent pile-shape quantification for autonomous wheel loaders2011In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2011, p. 4078-4083Conference paper (Refereed)
    Abstract [en]

    This paper presents a study of approaches for selecting an efficient attack pose when loading piled materials with industrial construction vehicles. Automated handling of piled materials is a highly desired goal in many construction and mining applications. The main contributions of the paper are an experimental study of two novel approaches for selecting an attack pose from 3D data, compared to previously published approaches and extensions thereof. The outcome is based on quantitative validation, both with simulated data and data from a real-world scenario with nontrivial ground geometry.

    Download full text (pdf)
    Consistent Pile-Shape Quantification for Autonomous Wheel Loaders
  • 43.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Nüchter, A.
    Jacobs University Bremen, Bremen, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Appearance-based loop detection from 3D laser data using the normal distributions transform2009In: IEEE International Conference on Robotics and Automation 2009 (ICRA '09), IEEE conference proceedings, 2009, p. 23-28Conference paper (Other academic)
    Abstract [en]

    We propose a new approach to appearance based loop detection from metric 3D maps, exploiting the NDT surface representation. Locations are described with feature histograms based on surface orientation and smoothness, and loop closure can be detected by matching feature histograms. We also present a quantitative performance evaluation using two realworld data sets, showing that the proposed method works well in different environments.© 2009 IEEE.

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    Appearance-Based Loop Detection from 3D Laser Data Using the Normal Distributions Transform
  • 44.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Nüchter, Andreas
    Jacobs University Bremen.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Automatic appearance-based loop detection from three-dimensional laser data using the normal distributions transform2009In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 26, no 11-12, p. 892-914Article in journal (Refereed)
    Abstract [en]

    We propose a new approach to appearance-based loop detection for mobile robots, usingthree-dimensional (3D) laser scans. Loop detection is an important problem in the simultaneouslocalization and mapping (SLAM) domain, and, because it can be seen as theproblem of recognizing previously visited places, it is an example of the data associationproblem. Without a flat-floor assumption, two-dimensional laser-based approaches arebound to fail in many cases. Two of the problems with 3D approaches that we address inthis paper are how to handle the greatly increased amount of data and how to efficientlyobtain invariance to 3D rotations.We present a compact representation of 3D point cloudsthat is still discriminative enough to detect loop closures without false positives (i.e.,detecting loop closure where there is none). A low false-positive rate is very important becausewrong data association could have disastrous consequences in a SLAM algorithm.Our approach uses only the appearance of 3D point clouds to detect loops and requires nopose information. We exploit the normal distributions transform surface representationto create feature histograms based on surface orientation and smoothness. The surfaceshape histograms compress the input data by two to three orders of magnitude. Becauseof the high compression rate, the histograms can be matched efficiently to compare theappearance of two scans. Rotation invariance is achieved by aligning scans with respectto dominant surface orientations. We also propose to use expectation maximization to fit a gamma mixture model to the output similarity measures in order to automatically determinethe threshold that separates scans at loop closures from nonoverlapping ones.Wediscuss the problem of determining ground truth in the context of loop detection and thedifficulties in comparing the results of the few available methods based on range information.Furthermore, we present quantitative performance evaluations using three realworlddata sets, one of which is highly self-similar, showing that the proposed methodachieves high recall rates (percentage of correctly identified loop closures) at low falsepositiverates in environments with different characteristics.

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    FULLTEXT01
  • 45.
    Magnusson, Martin
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    A comparison of 3D registration algorithms for autonomous underground mining vehicles2005Conference paper (Refereed)
    Abstract [en]

    The ICP algorithm and its derivatives is the de facto standard for registration of 3D range-finder scans today. This paper presents a quantitative comparison between ICP and 3D NDT, a novel approach based on the normal distributions transform. The new method ad- dresses two of the main problems of ICP: the fact that it does not make use of the local surface shape and the computationally demanding nearest-neighbour search. The results show that 3D NDT produces accurate results much faster, though it is more sensitive to error in the initial pose estimate.

  • 46.
    Magnusson, Martin
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    Elsrud, Rolf
    ̈Atlas Copco Rock Drills.
    Skagerlund, Lars-Erik
    Optab.
    3D modelling for underground mining vehicles2005Conference paper (Refereed)
    Abstract [en]

    This paper presents the basis of a new system for making detailed 3D models of underground tunnels. The system is to be used for automated control of mining vehicles. We describe some alternative methods for matching several partial scans, and their applicability for making a complete model of a mine environment

  • 47.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Kucner, Tomasz
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Quantitative Evaluation of Coarse-To-Fine Loading Strategies for Material Rehandling2015In: Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE), New York: IEEE conference proceedings , 2015, p. 450-455Conference paper (Refereed)
    Abstract [en]

    Autonomous handling of piled materials is an emerging topic in automation science and engineering. A central question for material rehandling tasks (transporting materials that have been assembled in piles) is “where to dig, in order to optimise performance”? In particular, we are interested in the application of autonomous wheel loaders to handle piles of gravel. Still, the methodology proposed in this paper relates to granular materials in other applications too. Although initial work on suggesting strategies for where to dig has been done by a few other groups, there has been a lack of structured evaluation of the usefulness of the proposed strategies. In an attempt to further the field, we present a quantitative evaluation of loading strategies; both coarse ones, aiming to maintain a good pile shape over long-term operation; and refined ones, aiming to detect the locally best attack pose for acquiring a good fill grade in the bucket. Using real-world data from a semi-automated test platform, we present an assessment of how previously proposed pile shape measures can be mapped to the amount of material in the bucket after loading. We also present experimental data for long-term strategies, using simulations based on real-world 3D scan data from a production site.

  • 48.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Gholami Shahbandi, Saeed
    IS lab, Halmstad University, Halmstad, Sweden.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Semi-Supervised 3D Place Categorisation by Descriptor Clustering2017In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 620-625Conference paper (Refereed)
    Abstract [en]

    Place categorisation; i. e., learning to group perception data into categories based on appearance; typically uses supervised learning and either visual or 2D range data.

    This paper shows place categorisation from 3D data without any training phase. We show that, by leveraging the NDT histogram descriptor to compactly encode 3D point cloud appearance, in combination with standard clustering techniques, it is possible to classify public indoor data sets with accuracy comparable to, and sometimes better than, previous supervised training methods. We also demonstrate the effectiveness of this approach to outdoor data, with an added benefit of being able to hierarchically categorise places into sub-categories based on a user-selected threshold.

    This technique relieves users of providing relevant training data, and only requires them to adjust the sensitivity to the number of place categories, and provide a semantic label to each category after the process is completed.

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    fulltext
  • 49.
    Magnusson, Martin
    et al.
    Örebro University, Department of Technology.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Duckett, Tom
    Department of Computing and Informatics, University of Lincoln, Lincoln, United Kingdom.
    Scan registration for autonomous mining vehicles using 3D-NDT2007In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 24, no 10, p. 803-827Article in journal (Refereed)
    Abstract [en]

    Scan registration is an essential sub-task when building maps based on range finder data from mobile robots. The problem is to deduce how the robot has moved between consecutive scans, based on the shape of overlapping portions of the scans. This paper presents a new algorithm for registration of 3D data. The algorithm is a generalisation and improvement of the normal distributions transform (NDT) for 2D data developed by Biber and Straßer, which allows for accurate registration using a memory-efficient representation of the scan surface. A detailed quantitative and qualitative comparison of the new algorithm with the 3D version of the popular ICP (iterative closest point) algorithm is presented. Results with actual mine data, some of which were collected with a new prototype 3D laser scanner, show that the presented algorithm is faster and slightly more reliable than the standard ICP algorithm for 3D registration, while using a more memory-efficient scan surface representation.

    Download full text (pdf)
    Scan Registration for Autonomous Mining Vehicles Using 3D-NDT
  • 50.
    Magnusson, Martin
    et al.
    Örebro University, Department of Technology.
    Nüchter, Andreas
    Institute of Computer Science, University of Osnabrück, Osnabrück, Germany.
    Lörken, Christopher
    Institute of Computer Science, University of Osnabrück, Osnabrück, Germany.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Hertzberg, Joachim
    Institute of Computer Science, University of Osnabrück, Osnabrück, Germany.
    3D mapping the Kvarntorp mine: a rield experiment for evaluation of 3D scan matching algorithms2008In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop, 2008Conference paper (Other academic)
    Abstract [en]

    This paper presents the results of a field experiment in the Kvarntorp mine outside of Örebro in Sweden. 3D mapping of the underground mine has been used to compare two scan matching methods, namely the iterative closest point algorithm (ICP) and the normal distributions transform (NDT). The experimental results of the algorithm are compared in terms of robustness and speed. For robustness we measure how reliably 3D scans are registered with respect to different starting pose estimates. Speed is evaluated running the authors’ best implementations on the same hardware. This leads to an unbiased comparison. In these experiments, NDT was shown to converge form a larger range of initial pose estimates than ICP, and to perform faster.

    Download full text (pdf)
    3D Mapping the Kvarntorp Mine: A Field Experiment for Evaluation of 3D Scan Matching Algorithms
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