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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.18174 |
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| _version_ | 1866910718671978496 |
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| author | Dong, Yangrui Gong, Weisheng Li, Qingyong Su, Kaijie He, Chen Wang, Z. Jane |
| author_facet | Dong, Yangrui Gong, Weisheng Li, Qingyong Su, Kaijie He, Chen Wang, Z. Jane |
| contents | This paper proposes an enhancement to the ORB-SLAM3 algorithm, tailored for applications on rugged road surfaces. Our improved algorithm adeptly combines feature point matching with optical flow methods, capitalizing on the high robustness of optical flow in complex terrains and the high precision of feature points on smooth surfaces. By refining the inter-frame matching logic of ORB-SLAM3, we have addressed the issue of frame matching loss on uneven roads. To prevent a decrease in accuracy, an adaptive matching mechanism has been incorporated, which increases the reliance on optical flow points during periods of high vibration, thereby effectively maintaining SLAM precision. Furthermore, due to the scarcity of multi-sensor datasets suitable for environments with bumpy roads or speed bumps, we have collected LiDAR and camera data from such settings. Our enhanced algorithm, ORB-SLAM3AB, was then benchmarked against several advanced open-source SLAM algorithms that rely solely on laser or visual data. Through the analysis of Absolute Trajectory Error (ATE) and Relative Pose Error (RPE) metrics, our results demonstrate that ORB-SLAM3AB achieves superior robustness and accuracy on rugged road surfaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18174 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ORB-SLAM3AB: Augmenting ORB-SLAM3 to Counteract Bumps with Optical Flow Inter-frame Matching Dong, Yangrui Gong, Weisheng Li, Qingyong Su, Kaijie He, Chen Wang, Z. Jane Robotics This paper proposes an enhancement to the ORB-SLAM3 algorithm, tailored for applications on rugged road surfaces. Our improved algorithm adeptly combines feature point matching with optical flow methods, capitalizing on the high robustness of optical flow in complex terrains and the high precision of feature points on smooth surfaces. By refining the inter-frame matching logic of ORB-SLAM3, we have addressed the issue of frame matching loss on uneven roads. To prevent a decrease in accuracy, an adaptive matching mechanism has been incorporated, which increases the reliance on optical flow points during periods of high vibration, thereby effectively maintaining SLAM precision. Furthermore, due to the scarcity of multi-sensor datasets suitable for environments with bumpy roads or speed bumps, we have collected LiDAR and camera data from such settings. Our enhanced algorithm, ORB-SLAM3AB, was then benchmarked against several advanced open-source SLAM algorithms that rely solely on laser or visual data. Through the analysis of Absolute Trajectory Error (ATE) and Relative Pose Error (RPE) metrics, our results demonstrate that ORB-SLAM3AB achieves superior robustness and accuracy on rugged road surfaces. |
| title | ORB-SLAM3AB: Augmenting ORB-SLAM3 to Counteract Bumps with Optical Flow Inter-frame Matching |
| topic | Robotics |
| url | https://arxiv.org/abs/2411.18174 |