ClusterMap Building and Relocalization in Urban Environments for Unmanned Vehicles

Pan, Zhichen; Chen, Haoyao; Li, Silin; Liu, Yunhui

doi:10.3390/s19194252

Cited by 7 publications

(6 citation statements)

References 31 publications

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“…Furthermore, Pan et al (2019) and Ding et al (2020) leverage clustering properties of the observations evaluating the observations count. Pan et al (2019) segment the points of a 3D LiDAR point cloud into different clusters. The proposed segmentation assumes that dynamic points do not appear frequently in the same place.…”

Section: Discussionmentioning

confidence: 99%

A systematic literature review on long‐term localization and mapping for mobile robots

Sousa

Sobreira

Moreira

2023

Journal of Field Robotics

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Long‐term operation of robots creates new challenges to Simultaneous Localization and Mapping (SLAM) algorithms. Long‐term SLAM algorithms should adapt to recent changes while preserving older states, when dealing with appearance variations (lighting, daytime, weather, or seasonal) or environment reconfiguration. When also operating robots for long periods and trajectory lengths, the map should readjust to environment changes but not grow indefinitely. The map size should depend only on updating the map with new information of interest, not on the operation time or trajectory length. Although several studies in the literature review SLAM algorithms, none of the studies focus on the challenges associated to lifelong SLAM. Thus, this paper presents a systematic literature review on long‐term localization and mapping following the Preferred Reporting Items for Systematic reviews and Meta‐Analysis guidelines. The review analyzes 142 works covering appearance invariance, modeling the environment dynamics, map size management, multisession, and computational topics such as parallel computing and timing efficiency. The analysis also focus on the experimental data and evaluation metrics commonly used to assess long‐term autonomy. Moreover, an overview over the bibliographic data of the 142 records provides analysis in terms of keywords and authorship co‐occurrence to identify the terms more used in long‐term SLAM and research networks between authors, respectively. Future studies can update this paper thanks to the systematic methodology presented in the review and the public GitHub repository with all the documentation and scripts used during the review process.

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Section: Discussionmentioning

confidence: 99%

A systematic literature review on long‐term localization and mapping for mobile robots

Sousa

Sobreira

Moreira

2023

Journal of Field Robotics

View full text Add to dashboard Cite

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“…The number of observations in different sessions combined with its consistency relative to its neighbors determine if a map point is static throughout the sessions. Both representations of the environment in Pan et al 2019 andDing, Y. Wang, Xiong, et al 2020 were stable to structural changes in the environment.…”

Section: Map Matchingmentioning

confidence: 93%

“…minimizes the Gibbs energy defined on the proposed Long-term Consistent Conditional Random Field (LC-CRF) for detecting dynamic points, considering that these points have often a large reprojection error in frame-to-map matching and points tend to have the same dynamic properties as the neighbor ones. In a dynamic scene, LC-CRF achieved lower ATE than ORB-SLAM(Mur-Artal, Montiel, et al 2015).Furthermore,Pan et al 2019 andDing, Y. Wang, Xiong, et al 2020 leverage clustering properties of the observations evaluating the observations count. Pan et al 2019 segments the points of a 3D LiDAR point cloud into different clusters assuming that dy-namic points do not appear frequently in the same place.…”

mentioning

confidence: 99%

A Systematic Literature Review on Long-Term Localization and Mapping for Mobile Robots

Sousa

Sobreira

Moreira

2022

Preprint

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Long-term operation of autonomous robots creates new challenges to the Simultaneous Localization and Mapping (SLAM). Varying conditions of the vehicle’s surroundings, such as appearance variations (lighting, daytime, weather, or seasonal) or reconfigurations of the environment, are a challenge for SLAM algorithms to adapt to new changes while preserving old states. When also operating for long periods and trajectory lengths, the map should readjust to environment changes but not grow indefinitely, where the map size should be dependent only on the explored environment area. Long-term SLAM intends to overcome the challenges associated with lifelong autonomy and improve the robustness of autonomous systems. Although several studies review SLAM algorithms, none of them focus on lifelong autonomy. Thus, this paper presents a systematic literature review on long-term localization and mapping following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. The review analyzes 142 works covering appearance invariance, modeling the environment dynamics, map size management, multi-session, and computational issues including parallel computing and timming efficiency. The analysis also focus on the experimental data and evaluation metrics commonly used to assess long-term autonomy. Moreover, an overview over the bibliographic data of the 142 records provides analysis in terms of keywords and authorship co-occurrence to identify the terms more used in long-term SLAM and research networks between authors, respectively. Future studies can update this paper thanks to the systematic methodology presented in the review and the public GitHub repository with all the documentation and scripts used during the review process.

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“…The quality of the registration algorithm dominates the quality of the high-resolution 3D map. Moreover, point cloud registration between real-time point cloud of a vehicle and 3D map can apply for real-time vehicle localization [74], [76]. There is a review of 3D point cloud processing and learning for autonomous driving [13].…”

Section: Autonomous Drivingmentioning

confidence: 99%

A comprehensive survey on point cloud registration

Huang

Mei

Zhang

et al. 2021

Preprint

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Registration is a transformation estimation problem between two point clouds, which has a unique and critical role in numerous computer vision applications. The developments of optimization-based methods and deep learning methods have improved registration robustness and efficiency. Recently, the combinations of optimization-based and deep learning methods have further improved performance. However, the connections between optimization-based and deep learning methods are still unclear. Moreover, with the recent development of 3D sensors and 3D reconstruction techniques, a new research direction emerges to align cross-source point clouds. This survey conducts a comprehensive survey, including both same-source and cross-source registration methods, and summarize the connections between optimization-based and deep learning methods, to provide further research insight. This survey also builds a new benchmark to evaluate the state-of-the-art registration algorithms in solving cross-source challenges. Besides, this survey summarizes the benchmark data sets and discusses point cloud registration applications across various domains. Finally, this survey proposes potential research directions in this rapidly growing field.

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ClusterMap Building and Relocalization in Urban Environments for Unmanned Vehicles

Cited by 7 publications

References 31 publications

A systematic literature review on long‐term localization and mapping for mobile robots

A systematic literature review on long‐term localization and mapping for mobile robots

A Systematic Literature Review on Long-Term Localization and Mapping for Mobile Robots

A comprehensive survey on point cloud registration

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