A survey on deep learning-based lidar place recognition
LiDAR-based place recognition (LPR) technology processes 3D LiDAR point clouds and encodes them into feature descriptors, enabling mobile robots to recognize previously visited locations. This capability supports critical tasks such as loop closure detection and re-localization. With the rapid advan...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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ELS Publishing (ELSP)
2025-03-01
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| Series: | Artificial Intelligence and Autonomous Systems |
| Subjects: | |
| Online Access: | https://elsp-homepage.oss-cn-hongkong.aliyuncs.compaper/journal/open/AIAS/2025/aias20250003.pdf |
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| _version_ | 1849239745343782912 |
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| author | Weizhong Jiang Shubin Si Hanzhang Xue Yiming Nie Zhipeng Xiao Qi Zhu Liang Xiao |
| author_facet | Weizhong Jiang Shubin Si Hanzhang Xue Yiming Nie Zhipeng Xiao Qi Zhu Liang Xiao |
| author_sort | Weizhong Jiang |
| collection | DOAJ |
| description | LiDAR-based place recognition (LPR) technology processes 3D LiDAR point clouds and encodes them into feature descriptors, enabling mobile robots to recognize previously visited locations. This capability supports critical tasks such as loop closure detection and re-localization. With the rapid advancements in deep learning, deep learning-based LiDAR place recognition (DL-LPR) has emerged as the dominant research direction in this field. However, existing reviews on DL-LPR remain limited in scope. To address this gap, this paper focuses on DL-LPR, introducing its core concepts, system structures, and applications. It presents a coarse-to-fine classification framework to systematically categorize and review existing methods, based on two dimensions: input data structure and model architecture. Furthermore, this paper summarizes commonly used datasets and performance evaluation metrics, along with performance comparisons of representative methods. Finally, it provides an in-depth analysis of the challenges faced by DL-LPR in complex environments, such as long-term, large-scale, and dynamic settings, and offers insights into future development trends. |
| format | Article |
| id | doaj-art-ad6f3bd0a22c4302aff8b184384aef1d |
| institution | Kabale University |
| issn | 2959-0744 2959-0752 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | ELS Publishing (ELSP) |
| record_format | Article |
| series | Artificial Intelligence and Autonomous Systems |
| spelling | doaj-art-ad6f3bd0a22c4302aff8b184384aef1d2025-08-20T04:00:51ZengELS Publishing (ELSP)Artificial Intelligence and Autonomous Systems2959-07442959-07522025-03-012114310.55092/aias202500031866020103581896704A survey on deep learning-based lidar place recognitionWeizhong Jiang0Shubin Si1Hanzhang Xue2Yiming Nie3Zhipeng Xiao4Qi Zhu5Liang Xiao6Unmanned Systems Technology Research Center, Defense Innovation Institute, Beijing, ChinaUnmanned Systems Technology Research Center, Defense Innovation Institute, Beijing, ChinaTest Center, National University of Defense Technology, Xi’an, ChinaUnmanned Systems Technology Research Center, Defense Innovation Institute, Beijing, ChinaUnmanned Systems Technology Research Center, Defense Innovation Institute, Beijing, ChinaUnmanned Systems Technology Research Center, Defense Innovation Institute, Beijing, ChinaUnmanned Systems Technology Research Center, Defense Innovation Institute, Beijing, ChinaLiDAR-based place recognition (LPR) technology processes 3D LiDAR point clouds and encodes them into feature descriptors, enabling mobile robots to recognize previously visited locations. This capability supports critical tasks such as loop closure detection and re-localization. With the rapid advancements in deep learning, deep learning-based LiDAR place recognition (DL-LPR) has emerged as the dominant research direction in this field. However, existing reviews on DL-LPR remain limited in scope. To address this gap, this paper focuses on DL-LPR, introducing its core concepts, system structures, and applications. It presents a coarse-to-fine classification framework to systematically categorize and review existing methods, based on two dimensions: input data structure and model architecture. Furthermore, this paper summarizes commonly used datasets and performance evaluation metrics, along with performance comparisons of representative methods. Finally, it provides an in-depth analysis of the challenges faced by DL-LPR in complex environments, such as long-term, large-scale, and dynamic settings, and offers insights into future development trends.https://elsp-homepage.oss-cn-hongkong.aliyuncs.compaper/journal/open/AIAS/2025/aias20250003.pdfplace recognitionlidardeep learningmobile robotsnavigationre-localizationloop closure detection |
| spellingShingle | Weizhong Jiang Shubin Si Hanzhang Xue Yiming Nie Zhipeng Xiao Qi Zhu Liang Xiao A survey on deep learning-based lidar place recognition Artificial Intelligence and Autonomous Systems place recognition lidar deep learning mobile robots navigation re-localization loop closure detection |
| title | A survey on deep learning-based lidar place recognition |
| title_full | A survey on deep learning-based lidar place recognition |
| title_fullStr | A survey on deep learning-based lidar place recognition |
| title_full_unstemmed | A survey on deep learning-based lidar place recognition |
| title_short | A survey on deep learning-based lidar place recognition |
| title_sort | survey on deep learning based lidar place recognition |
| topic | place recognition lidar deep learning mobile robots navigation re-localization loop closure detection |
| url | https://elsp-homepage.oss-cn-hongkong.aliyuncs.compaper/journal/open/AIAS/2025/aias20250003.pdf |
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