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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2307.08221 |
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| _version_ | 1866910375466762240 |
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| author | Liao, Lizhou Yan, Wenlei Sun, Li Bai, Xinhui You, Zhenxing Yuan, Hongyuan Fu, Chunyun |
| author_facet | Liao, Lizhou Yan, Wenlei Sun, Li Bai, Xinhui You, Zhenxing Yuan, Hongyuan Fu, Chunyun |
| contents | Loop-closure detection, also known as place recognition, aiming to identify previously visited locations, is an essential component of a SLAM system. Existing research on lidar-based loop closure heavily relies on dense point cloud and 360 FOV lidars. This paper proposes an out-of-the-box NDT (Normal Distribution Transform) based global descriptor, NDT-Map-Code, designed for both on-road driving and underground valet parking scenarios. NDT-Map-Code can be directly extracted from the NDT map without the need for a dense point cloud, resulting in excellent scalability and low maintenance cost. The NDT representation is leveraged to identify representative patterns, which are further encoded according to their spatial location (bearing, range, and height). Experimental results on the NIO underground parking lot dataset and the KITTI dataset demonstrate that our method achieves significantly better performance compared to the state-of-the-art. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_08221 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | NDT-Map-Code: A 3D global descriptor for real-time loop closure detection in lidar SLAM Liao, Lizhou Yan, Wenlei Sun, Li Bai, Xinhui You, Zhenxing Yuan, Hongyuan Fu, Chunyun Robotics Loop-closure detection, also known as place recognition, aiming to identify previously visited locations, is an essential component of a SLAM system. Existing research on lidar-based loop closure heavily relies on dense point cloud and 360 FOV lidars. This paper proposes an out-of-the-box NDT (Normal Distribution Transform) based global descriptor, NDT-Map-Code, designed for both on-road driving and underground valet parking scenarios. NDT-Map-Code can be directly extracted from the NDT map without the need for a dense point cloud, resulting in excellent scalability and low maintenance cost. The NDT representation is leveraged to identify representative patterns, which are further encoded according to their spatial location (bearing, range, and height). Experimental results on the NIO underground parking lot dataset and the KITTI dataset demonstrate that our method achieves significantly better performance compared to the state-of-the-art. |
| title | NDT-Map-Code: A 3D global descriptor for real-time loop closure detection in lidar SLAM |
| topic | Robotics |
| url | https://arxiv.org/abs/2307.08221 |