| 1. |
- Molina, Sergi, et al.
(författare)
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The ILIAD Safety Stack : Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots
- 2023
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Ingår i: IEEE robotics & automation magazine. - : IEEE. - 1070-9932 .- 1558-223X.
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Adolfsson, Daniel, 1992-, et al.
(författare)
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CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry
- 2021
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Ingår i: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021). - : IEEE. - 9781665417143 - 9781665417150 ; , s. 5462-5469
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 3. |
- Adolfsson, Daniel, 1992-, et al.
(författare)
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CorAl : Introspection for robust radar and lidar perception in diverse environments using differential entropy
- 2022
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Ingår i: Robotics and Autonomous Systems. - : Elsevier. - 0921-8890 .- 1872-793X. ; 155
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Adolfsson, Daniel, 1992-, et al.
(författare)
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CorAl – Are the point clouds Correctly Aligned?
- 2021
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Ingår i: 10th European Conference on Mobile Robots (ECMR 2021). - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 5. |
- Adolfsson, Daniel, 1992-, et al.
(författare)
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Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments
- 2023
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Ingår i: IEEE Transactions on robotics. - : IEEE. - 1552-3098 .- 1941-0468. ; 39:2, s. 1476-1495
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- Adolfsson, Daniel, 1992-, et al.
(författare)
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Oriented surface points for efficient and accurate radar odometry
- 2021
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 7. |
- Adolfsson, Daniel, 1992-
(författare)
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Robust large-scale mapping and localization : Combining robust sensing and introspection
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The presence of autonomous systems is rapidly increasing in society and industry. To achieve successful, efficient, and safe deployment of autonomous systems, they must be navigated by means of highly robust localization systems. Additionally, these systems need to localize accurately and efficiently in realtime under adverse environmental conditions, and within considerably diverse and new previously unseen environments.This thesis focuses on investigating methods to achieve robust large-scale localization and mapping, incorporating robustness at multiple stages. Specifically, the research explores methods with sensory robustness, utilizing radar, which exhibits tolerance to harsh weather, dust, and variations in lighting conditions. Furthermore, the thesis presents methods with algorithmic robustness, which prevent failures by incorporating introspective awareness of localization quality. This thesis aims to answer the following research questions:How can radar data be efficiently filtered and represented for robust radar odometry? How can accurate and robust odometry be achieved with radar? How can localization quality be assessed and leveraged for robust detection of localization failures? How can self-awareness of localization quality be utilized to enhance the robustness of a localization system?While addressing these research questions, this thesis makes the following contributions to large-scale localization and mapping: A method for robust and efficient radar processing and state-of-the-art odometry estimation, and a method for self-assessment of localization quality and failure detection in lidar and radar localization. Self-assessment of localization quality is integrated into robust systems for large-scale Simultaneous Localization And Mapping, and rapid global localization in prior maps. These systems leverage self-assessment of localization quality to improve performance and prevent failures in loop closure and global localization, and consequently achieve safe robot localization.The methods presented in this thesis were evaluated through comparative assessments of public benchmarks and real-world data collected from various industrial scenarios. These evaluations serve to validate the effectiveness and reliability of the proposed approaches. As a result, this research represents a significant advancement toward achieving highly robust localization capabilities with broad applicability.
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| 8. |
- Adolfsson, Daniel, 1992-, et al.
(författare)
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TBV Radar SLAM - Trust but Verify Loop Candidates
- 2023
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 8:6, s. 3613-3620
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Tidskriftsartikel (refereegranskat)abstract
- 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
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| 9. |
- Alhashimi, Anas, 1978-, et al.
(författare)
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BFAR – Bounded False Alarm Rate detector for improved radar odometry estimation
- 2021
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Konferensbidrag (refereegranskat)abstract
- 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%.
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| 10. |
- Gupta, Himanshu, 1993-, et al.
(författare)
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Revisiting Distribution-Based Registration Methods
- 2023
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Ingår i: 2023 European Conference on Mobile Robots (ECMR). - : IEEE. - 9798350307047 - 9798350307054 ; , s. 43-48
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 11. |
- Sun, L., et al.
(författare)
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Localising Faster : Efficient and precise lidar-based robot localisation in large-scale environments
- 2020
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Ingår i: 2020 IEEE International Conference on Robotics and Automation (ICRA). - : IEEE. - 9781728173962 - 9781728173955 ; , s. 4386-4392
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a novel approach for global localisation of mobile robots in large-scale environments. Our method leverages learning-based localisation and filtering-based localisation, to localise the robot efficiently and precisely through seeding Monte Carlo Localisation (MCL) with a deeplearned distribution. In particular, a fast localisation system rapidly estimates the 6-DOF pose through a deep-probabilistic model (Gaussian Process Regression with a deep kernel), then a precise recursive estimator refines the estimated robot pose according to the geometric alignment. More importantly, the Gaussian method (i.e. deep probabilistic localisation) and nonGaussian method (i.e. MCL) can be integrated naturally via importance sampling. Consequently, the two systems can be integrated seamlessly and mutually benefit from each other. To verify the proposed framework, we provide a case study in large-scale localisation with a 3D lidar sensor. Our experiments on the Michigan NCLT long-term dataset show that the proposed method is able to localise the robot in 1.94 s on average (median of 0.8 s) with precision 0.75 m in a largescale environment of approximately 0.5 km 2.
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| 12. |
- Moraes Holst, Luiza, et al.
(författare)
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Downregulated Mucosal Autophagy, Alpha Kinase-1 and IL-17 Signaling Pathways in Active and Quiescent Ulcerative Colitis
- 2022
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Ingår i: Clinical and Experimental Gastroenterology. - : DOVE MEDICAL PRESS LTD. - 1178-7023. ; 15, s. 129-144
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Tidskriftsartikel (refereegranskat)abstract
- Background: Improved mucosal immune profiling in active and quiescent colonic inflammatory bowel disease (IBD) is needed to develop therapeutic options for treating and preventing flares. This study therefore aimed to provide a comprehensive mucosal characterization with emphasis on immunological host response of patients with active ulcerative colitis (UC active), UC during remission (UC remission) and active colonic Crohn's disease (CD active).Methods: Colonic biopsies from 47 study subjects were collected for gene expression and pathway analyses using the NanoString host-response panel, including 776 genes and 56 immune-related pathways.Results: The majority of mucosal gene expression and signaling pathway scores were increased in active IBD (n=27) compared to healthy subjects (n=10). However, both active IBD and UC remission (n=10) demonstrated decreased gene expression and signaling pathway scores related to autophagy, alpha kinase-1 and IL-17 signaling pathways compared to healthy subjects. Further, UC remission was characterized by decreased scores of several signaling pathways linked to homeostasis along with increased mononuclear cell migration pathway score as compared to healthy subjects. No major differences in the colonic mucosal gene expression between CD active (n=7) and UC (n=20) active were observed.Conclusion: This study indicates that autophagy, alpha kinase-1 and IL-17 signaling pathways are persistently downregulated in UC irrespective of disease activity. Further, UC patients in remission present a unique mucosal environment, potentially preventing patients from reaching and sustaining true homeostasis. These findings may enable better comprehension of the remitting and relapsing pattern of colonic IBD and guide future treatment and prevention of flares.
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| 13. |
- Schröder, Karin, 1966-, et al.
(författare)
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Effectiveness and Quality of Implementing a Best Practice Model of Care for Low Back Pain (BetterBack) Compared with Routine Care in Physiotherapy : A Hybrid Type 2 Trial
- 2021
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Ingår i: Journal of Clinical Medicine. - : MDPI. - 2077-0383. ; 10:6, s. 1230-
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Tidskriftsartikel (refereegranskat)abstract
- Low back pain (LBP) occurs in all ages and first-line treatment by physiotherapists is common. The main aim of the current study was to evaluate the effectiveness of implementing a best practice model of care for LBP (intervention group—BetterBackJ MoC) compared to routine physiotherapy care (control group) regarding longitudinal patient reported outcomes. The BetterBackJ MoC contains clinical guideline recommendations and support tools to facilitate clinician adherence to guidelines. A secondary exploratory aim was to compare patient outcomes based on the fidelity of fulfilling a clinical practice quality index regarding physiotherapist care. A stepped cluster randomized design nested patients with LBP in the three clusters which were allocated to control (n = 203) or intervention (n = 264). Patient reported measures were collected at baseline, 3, 6 and 12 months and analyzed with mixed model regression. The primary outcome was between-group changes from baseline to 3 months for pain intensity and disability. Implementation of the BetterBackJ MoC did not show any between-group differences in the primary outcomes compared with routine care. However, the intervention group showed significantly higher satisfaction at 3 months and clinically meaningful greater improvement in LBP illness perception at 3 months and quality of life at 3 and 6 months but not in patient enablement and global impression of change compared with the control group. Physiotherapists’ care that adhered to all clinical practice quality indices resulted in an improvement of most patient reported outcomes with a clinically meaningful greater improved LBP illness perception at 3 month and quality of life at 3 and 6 months, significantly greater improvement in LBP illness perception, pain and satisfaction at 3 and 6 months and significantly better enablement at all time points as well as better global improvement outcomes at 3 months compared with non-adherent care. This highlights the importance of clinical guideline based primary care for improving patient reported LBP outcomes.
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