Adaptive LiDAR-Radar Fusion for Outdoor Odometry Across Dense Smoke Conditions
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909175378870272 |
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| author | Noh, Chiyun Kim, Ayoung |
| author_facet | Noh, Chiyun Kim, Ayoung |
| contents | Robust odometry estimation in perceptually degraded environments represents a key challenge in the field of robotics. In this paper, we propose a LiDAR-radar fusion method for robust odometry for adverse environment with LiDAR degeneracy. By comparing the LiDAR point cloud with the radar static point cloud obtained through preprocessing module, it is possible to identify instances of LiDAR degeneracy to overcome perceptual limits. We demonstrate the effectiveness of our method in challenging conditions such as dense smoke, showcasing its ability to reliably estimate odometry and identify/remove dynamic points prone to LiDAR degeneracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17441 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive LiDAR-Radar Fusion for Outdoor Odometry Across Dense Smoke Conditions Noh, Chiyun Kim, Ayoung Robotics Robust odometry estimation in perceptually degraded environments represents a key challenge in the field of robotics. In this paper, we propose a LiDAR-radar fusion method for robust odometry for adverse environment with LiDAR degeneracy. By comparing the LiDAR point cloud with the radar static point cloud obtained through preprocessing module, it is possible to identify instances of LiDAR degeneracy to overcome perceptual limits. We demonstrate the effectiveness of our method in challenging conditions such as dense smoke, showcasing its ability to reliably estimate odometry and identify/remove dynamic points prone to LiDAR degeneracy. |
| title | Adaptive LiDAR-Radar Fusion for Outdoor Odometry Across Dense Smoke Conditions |
| topic | Robotics |
| url | https://arxiv.org/abs/2403.17441 |