3D-BBS: Global Localization for 3D Point Cloud Scan Matching Using Branch-and-Bound Algorithm
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866909355079630848 |
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| author | Aoki, Koki Koide, Kenji Oishi, Shuji Yokozuka, Masashi Banno, Atsuhiko Meguro, Junichi |
| author_facet | Aoki, Koki Koide, Kenji Oishi, Shuji Yokozuka, Masashi Banno, Atsuhiko Meguro, Junichi |
| contents | This paper presents an accurate and fast 3D global localization method, 3D-BBS, that extends the existing branch-and-bound (BnB)-based 2D scan matching (BBS) algorithm. To reduce memory consumption, we utilize a sparse hash table for storing hierarchical 3D voxel maps. To improve the processing cost of BBS in 3D space, we propose an efficient roto-translational space branching. Furthermore, we devise a batched BnB algorithm to fully leverage GPU parallel processing. Through experiments in simulated and real environments, we demonstrated that the 3D-BBS enabled accurate global localization with only a 3D LiDAR scan roughly aligned in the gravity direction and a 3D pre-built map. This method required only 878 msec on average to perform global localization and outperformed state-of-the-art global registration methods in terms of accuracy and processing speed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_10023 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | 3D-BBS: Global Localization for 3D Point Cloud Scan Matching Using Branch-and-Bound Algorithm Aoki, Koki Koide, Kenji Oishi, Shuji Yokozuka, Masashi Banno, Atsuhiko Meguro, Junichi Robotics This paper presents an accurate and fast 3D global localization method, 3D-BBS, that extends the existing branch-and-bound (BnB)-based 2D scan matching (BBS) algorithm. To reduce memory consumption, we utilize a sparse hash table for storing hierarchical 3D voxel maps. To improve the processing cost of BBS in 3D space, we propose an efficient roto-translational space branching. Furthermore, we devise a batched BnB algorithm to fully leverage GPU parallel processing. Through experiments in simulated and real environments, we demonstrated that the 3D-BBS enabled accurate global localization with only a 3D LiDAR scan roughly aligned in the gravity direction and a 3D pre-built map. This method required only 878 msec on average to perform global localization and outperformed state-of-the-art global registration methods in terms of accuracy and processing speed. |
| title | 3D-BBS: Global Localization for 3D Point Cloud Scan Matching Using Branch-and-Bound Algorithm |
| topic | Robotics |
| url | https://arxiv.org/abs/2310.10023 |