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Liao, Q., Sun, D., Zhang, S., Loutfi, A. & Andreasson, H. (2023). Fuzzy Cluster-based Group-wise Point Set Registration with Quality Assessment. IEEE Transactions on Image Processing, 32, 550-564
Open this publication in new window or tab >>Fuzzy Cluster-based Group-wise Point Set Registration with Quality Assessment
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2023 (English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 32, p. 550-564Article in journal (Refereed) Published
Abstract [en]

This article studies group-wise point set registration and makes the following contributions: "FuzzyGReg", which is a new fuzzy cluster-based method to register multiple point sets jointly, and "FuzzyQA", which is the associated quality assessment to check registration accuracy automatically. Given a group of point sets, FuzzyGReg creates a model of fuzzy clusters and equally treats all the point sets as the elements of the fuzzy clusters. Then, the group-wise registration is turned into a fuzzy clustering problem. To resolve this problem, FuzzyGReg applies a fuzzy clustering algorithm to identify the parameters of the fuzzy clusters while jointly transforming all the point sets to achieve an alignment. Next, based on the identified fuzzy clusters, FuzzyQA calculates the spatial properties of the transformed point sets and then checks the alignment accuracy by comparing the similarity degrees of the spatial properties of the point sets. When a local misalignment is detected, a local re-alignment is performed to improve accuracy. The proposed method is cost-efficient and convenient to be implemented. In addition, it provides reliable quality assessments in the absence of ground truth and user intervention. In the experiments, different point sets are used to test the proposed method and make comparisons with state-of-the-art registration techniques. The experimental results demonstrate the effectiveness of our method.The code is available at https://gitsvn-nt.oru.se/qianfang.liao/FuzzyGRegWithQA

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Quality assessment, Measurement, Three-dimensional displays, Registers, Probability distribution, Point cloud compression, Optimization, Group-wise registration, registration quality assessment, joint alignment, fuzzy clusters, 3D point sets
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-102755 (URN)10.1109/TIP.2022.3231132 (DOI)000908058200002 ()
Funder
Vinnova, 2019- 05878Swedish Research Council Formas, 2019-02264
Available from: 2022-12-16 Created: 2022-12-16 Last updated: 2023-04-03Bibliographically approved
Adolfsson, D., Magnusson, M., Alhashimi, A., Lilienthal, A. & Andreasson, H. (2023). Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments. IEEE Transactions on robotics, 39(2), 1476-1495
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
Gupta, H., Lilienthal, A., Andreasson, H. & Kurtser, P. (2023). NDT-6D for color registration in agri-robotic applications. Journal of Field Robotics, 40(6), 1603-1619
Open this publication in new window or tab >>NDT-6D for color registration in agri-robotic applications
2023 (English)In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 40, no 6, p. 1603-1619Article in journal (Refereed) Published
Abstract [en]

Registration of point cloud data containing both depth and color information is critical for a variety of applications, including in-field robotic plant manipulation, crop growth modeling, and autonomous navigation. However, current state-of-the-art registration methods often fail in challenging agricultural field conditions due to factors such as occlusions, plant density, and variable illumination. To address these issues, we propose the NDT-6D registration method, which is a color-based variation of the Normal Distribution Transform (NDT) registration approach for point clouds. Our method computes correspondences between pointclouds using both geometric and color information and minimizes the distance between these correspondences using only the three-dimensional (3D) geometric dimensions. We evaluate the method using the GRAPES3D data set collected with a commercial-grade RGB-D sensor mounted on a mobile platform in a vineyard. Results show that registration methods that only rely on depth information fail to provide quality registration for the tested data set. The proposed color-based variation outperforms state-of-the-art methods with a root mean square error (RMSE) of 1.1-1.6 cm for NDT-6D compared with 1.1-2.3 cm for other color-information-based methods and 1.2-13.7 cm for noncolor-information-based methods. The proposed method is shown to be robust against noises using the TUM RGBD data set by artificially adding noise present in an outdoor scenario. The relative pose error (RPE) increased similar to 14% for our method compared to an increase of similar to 75% for the best-performing registration method. The obtained average accuracy suggests that the NDT-6D registration methods can be used for in-field precision agriculture applications, for example, crop detection, size-based maturity estimation, and growth modeling.

Place, publisher, year, edition, pages
John Wiley & Sons, 2023
Keywords
agricultural robotics, color pointcloud, in-field sensing, machine perception, RGB-D registration, stereo IR, vineyard
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-106131 (URN)10.1002/rob.22194 (DOI)000991774400001 ()2-s2.0-85159844423 (Scopus ID)
Funder
EU, Horizon 2020
Available from: 2023-06-01 Created: 2023-06-01 Last updated: 2023-11-28Bibliographically approved
Gupta, H., Andreasson, H., Magnusson, M., Julier, S. & Lilienthal, A. J. (2023). Revisiting Distribution-Based Registration Methods. In: Marques, L.; Markovic, I. (Ed.), 2023 European Conference on Mobile Robots (ECMR): . Paper presented at 11th European Conference on Mobile Robots (ECMR 2023), Coimbra, Portugal, September 4-7, 2023 (pp. 43-48). IEEE
Open this publication in new window or tab >>Revisiting Distribution-Based Registration Methods
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2023 (English)In: 2023 European Conference on Mobile Robots (ECMR) / [ed] Marques, L.; Markovic, I., IEEE , 2023, p. 43-48Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2023
Series
European Conference on Mobile Robots, ISSN 2639-7919, E-ISSN 2767-8733
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-109681 (URN)10.1109/ECMR59166.2023.10256416 (DOI)001082260500007 ()2-s2.0-8517439971 (Scopus ID)9798350307047 (ISBN)9798350307054 (ISBN)
Conference
11th European Conference on Mobile Robots (ECMR 2023), Coimbra, Portugal, September 4-7, 2023
Funder
EU, Horizon 2020, 858101
Available from: 2023-11-15 Created: 2023-11-15 Last updated: 2023-11-15Bibliographically approved
Gupta, H., Andreasson, H., Lilienthal, A. J. & Kurtser, P. (2023). Robust Scan Registration for Navigation in Forest Environment Using Low-Resolution LiDAR Sensors. Sensors, 23(10), Article ID 4736.
Open this publication in new window or tab >>Robust Scan Registration for Navigation in Forest Environment Using Low-Resolution LiDAR Sensors
2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 10, article id 4736Article in journal (Refereed) Published
Abstract [en]

Automated forest machines are becoming important due to human operators' complex and dangerous working conditions, leading to a labor shortage. This study proposes a new method for robust SLAM and tree mapping using low-resolution LiDAR sensors in forestry conditions. Our method relies on tree detection to perform scan registration and pose correction using only low-resolution LiDAR sensors (16Ch, 32Ch) or narrow field of view Solid State LiDARs without additional sensory modalities like GPS or IMU. We evaluate our approach on three datasets, including two private and one public dataset, and demonstrate improved navigation accuracy, scan registration, tree localization, and tree diameter estimation compared to current approaches in forestry machine automation. Our results show that the proposed method yields robust scan registration using detected trees, outperforming generalized feature-based registration algorithms like Fast Point Feature Histogram, with an above 3 m reduction in RMSE for the 16Chanel LiDAR sensor. For Solid-State LiDAR the algorithm achieves a similar RMSE of 3.7 m. Additionally, our adaptive pre-processing and heuristic approach to tree detection increased the number of detected trees by 13% compared to the current approach of using fixed radius search parameters for pre-processing. Our automated tree trunk diameter estimation method yields a mean absolute error of 4.3 cm (RSME = 6.5 cm) for the local map and complete trajectory maps.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
tree segmentation, LiDAR mapping, forest inventory, SLAM, forestry robotics, scan registration
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:oru:diva-106315 (URN)10.3390/s23104736 (DOI)000997887900001 ()37430655 (PubMedID)2-s2.0-85160406537 (Scopus ID)
Funder
EU, Horizon 2020, 858101
Available from: 2023-06-19 Created: 2023-06-19 Last updated: 2023-07-12Bibliographically approved
Adolfsson, D., Karlsson, M., Kubelka, V., Magnusson, M. & Andreasson, H. (2023). TBV Radar SLAM - Trust but Verify Loop Candidates. IEEE Robotics and Automation Letters, 8(6), 3613-3620
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
Molina, S., Mannucci, A., Magnusson, M., Adolfsson, D., Andreasson, H., Hamad, M., . . . Lilienthal, A. J. (2023). The ILIAD Safety Stack: Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots. IEEE robotics & automation magazine
Open this publication in new window or tab >>The ILIAD Safety Stack: Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots
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2023 (English)In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

Current intralogistics services require keeping up with e-commerce demands, reducing delivery times and waste, and increasing overall flexibility. As a consequence, the use of automated guided vehicles (AGVs) and, more recently, autonomous mobile robots (AMRs) for logistics operations is steadily increasing.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Robots, Safety, Navigation, Mobile robots, Human-robot interaction, Hidden Markov models, Trajectory
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-108145 (URN)10.1109/MRA.2023.3296983 (DOI)001051249900001 ()
Funder
EU, Horizon 2020, 732737
Available from: 2023-09-14 Created: 2023-09-14 Last updated: 2024-01-02Bibliographically approved
Andreasson, H., Larsson, J. & Lowry, S. (2022). A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization. Sensors, 22(7), Article ID 2588.
Open this publication in new window or tab >>A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization
2022 (English)In: Sensors, E-ISSN 1424-8220, Vol. 22, no 7, article id 2588Article in journal (Refereed) Published
Abstract [en]

This paper presents a local planning approach that is targeted for pseudo-omnidirectional vehicles: that is, vehicles that can drive sideways and rotate on the spot. This local planner—MSDU–is based on optimal control and formulates a non-linear optimization problem formulation that exploits the omni-motion capabilities of the vehicle to drive the vehicle to the goal in a smooth and efficient manner while avoiding obstacles and singularities. MSDU is designed for a real platform for mobile manipulation where one key function is the capability to drive in narrow and confined areas. The real-world evaluations show that MSDU planned paths that were smoother and more accurate than a comparable local path planner Timed Elastic Band (TEB), with a mean (translational, angular) error for MSDU of (0.0028 m, 0.0010 rad) compared to (0.0033 m, 0.0038 rad) for TEB. MSDU also generated paths that were consistently shorter than TEB, with a mean (translational, angular) distance traveled of (0.6026 m, 1.6130 rad) for MSDU compared to (0.7346 m, 3.7598 rad) for TEB.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
local planning, optimal control, obstacle avoidance
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-98510 (URN)10.3390/s22072588 (DOI)000781087300001 ()35408204 (PubMedID)2-s2.0-85127034496 (Scopus ID)
Funder
Swedish Research Council Formas, 2019-02264
Available from: 2022-04-07 Created: 2022-04-07 Last updated: 2022-04-20Bibliographically approved
Seeburger, P., Herdenstam, A. P. F., Kurtser, P., Arunachalam, A., Castro Alves, V., Hyötyläinen, T. & Andreasson, H. (2022). Controlled mechanical stimuli reveal novel associations between basil metabolism and sensory quality. Food Chemistry, 404(Pt A), Article ID 134545.
Open this publication in new window or tab >>Controlled mechanical stimuli reveal novel associations between basil metabolism and sensory quality
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2022 (English)In: Food Chemistry, ISSN 0308-8146, E-ISSN 1873-7072, Vol. 404, no Pt A, article id 134545Article in journal (Refereed) Published
Abstract [en]

There is an increasing interest in the use of automation in plant production settings. Here, we employed a robotic platform to induce controlled mechanical stimuli (CMS) aiming to improve basil quality. Semi-targeted UHPLC-qToF-MS analysis of organic acids, amino acids, phenolic acids, and phenylpropanoids revealed changes in basil secondary metabolism under CMS, which appear to be associated with changes in taste, as revealed by different means of sensory evaluation (overall liking, check-all-that-apply, and just-about-right analysis). Further network analysis combining metabolomics and sensory data revealed novel links between plant metabolism and sensory quality. Amino acids and organic acids including maleic acid were negatively associated with basil quality, while increased levels of secondary metabolites, particularly linalool glucoside, were associated with improved basil taste. In summary, by combining metabolomics and sensory analysis we reveal the potential of automated CMS on crop production, while also providing new associations between plant metabolism and sensory quality.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Agricultural robotics, Linalool glucoside, Network analysis, Plant metabolomics, Sensomics, Sensory analysis
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-101814 (URN)10.1016/j.foodchem.2022.134545 (DOI)000873921900006 ()36252376 (PubMedID)2-s2.0-85139833699 (Scopus ID)
Funder
Örebro University
Note

Funding agency:

German Academic Exchange Service (Deutscher Akademischer Austauschdienst, DAAD)

Available from: 2022-10-18 Created: 2022-10-18 Last updated: 2022-11-15Bibliographically approved
Adolfsson, D., Castellano-Quero, M., Magnusson, M., Lilienthal, A. J. & Andreasson, H. (2022). CorAl: Introspection for robust radar and lidar perception in diverse environments using differential entropy. Robotics and Autonomous Systems, 155, Article ID 104136.
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
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ORCID iD: ORCID iD iconorcid.org/0000-0002-2953-1564

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