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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2505.09103 |
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| _version_ | 1866908364171116544 |
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| author | Xiang, Jianguang He, Xiaofeng Chen, Zizhuo Zhang, Lilian Luo, Xincan Mao, Jun |
| author_facet | Xiang, Jianguang He, Xiaofeng Chen, Zizhuo Zhang, Lilian Luo, Xincan Mao, Jun |
| contents | Recent advances in 4D radar-inertial odometry have demonstrated promising potential for autonomous lo calization in adverse conditions. However, effective handling of sparse and noisy radar measurements remains a critical challenge. In this paper, we propose a radar-inertial odometry with a spatial weighting method that adapts to unevenly distributed points and a novel point-description histogram for challenging point registration. To make full use of the Doppler velocity from different spatial sections, we propose a weighting calculation model. To enhance the point cloud registration performance under challenging scenarios, we con struct a novel point histogram descriptor that combines local geometric features and radar cross-section (RCS) features. We have also conducted extensive experiments on both public and self-constructed datasets. The results demonstrate the precision and robustness of the proposed VGC-RIO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09103 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VGC-RIO: A Tightly Integrated Radar-Inertial Odometry with Spatial Weighted Doppler Velocity and Local Geometric Constrained RCS Histograms Xiang, Jianguang He, Xiaofeng Chen, Zizhuo Zhang, Lilian Luo, Xincan Mao, Jun Robotics Recent advances in 4D radar-inertial odometry have demonstrated promising potential for autonomous lo calization in adverse conditions. However, effective handling of sparse and noisy radar measurements remains a critical challenge. In this paper, we propose a radar-inertial odometry with a spatial weighting method that adapts to unevenly distributed points and a novel point-description histogram for challenging point registration. To make full use of the Doppler velocity from different spatial sections, we propose a weighting calculation model. To enhance the point cloud registration performance under challenging scenarios, we con struct a novel point histogram descriptor that combines local geometric features and radar cross-section (RCS) features. We have also conducted extensive experiments on both public and self-constructed datasets. The results demonstrate the precision and robustness of the proposed VGC-RIO. |
| title | VGC-RIO: A Tightly Integrated Radar-Inertial Odometry with Spatial Weighted Doppler Velocity and Local Geometric Constrained RCS Histograms |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.09103 |