Omni-LIVO: Robust RGB-Colored Multi-Camera Visual-Inertial-LiDAR Odometry via Photometric Migration and ESIKF Fusion
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908918293200896 |
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| author | Cao, Yinong Zhang, Chenyang He, Xin Chen, Yuwei Pu, Chengyu Wang, Bingtao Wu, Kaile Zhu, Shouzheng Han, Fei Liu, Shijie Li, Chunlai Wang, Jianyu |
| author_facet | Cao, Yinong Zhang, Chenyang He, Xin Chen, Yuwei Pu, Chengyu Wang, Bingtao Wu, Kaile Zhu, Shouzheng Han, Fei Liu, Shijie Li, Chunlai Wang, Jianyu |
| contents | Wide field-of-view (FoV) LiDAR sensors provide dense geometry across large environments, but existing LiDAR-inertial-visual odometry (LIVO) systems generally rely on a single camera, limiting their ability to fully exploit LiDAR-derived depth for photometric alignment and scene colorization. We present Omni-LIVO, a tightly coupled multi-camera LIVO system that leverages multi-view observations to comprehensively utilize LiDAR geometric information across extended spatial regions. Omni-LIVO introduces a Cross-View direct alignment strategy that maintains photometric consistency across non-overlapping views, and extends the Error-State Iterated Kalman Filter (ESIKF) with multi-view updates and adaptive covariance. The system is evaluated on public benchmarks and our custom dataset, showing improved accuracy and robustness over state-of-the-art LIVO, LIO, and visual-inertial SLAM baselines. Code and dataset will be released upon publication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15673 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Omni-LIVO: Robust RGB-Colored Multi-Camera Visual-Inertial-LiDAR Odometry via Photometric Migration and ESIKF Fusion Cao, Yinong Zhang, Chenyang He, Xin Chen, Yuwei Pu, Chengyu Wang, Bingtao Wu, Kaile Zhu, Shouzheng Han, Fei Liu, Shijie Li, Chunlai Wang, Jianyu Robotics Wide field-of-view (FoV) LiDAR sensors provide dense geometry across large environments, but existing LiDAR-inertial-visual odometry (LIVO) systems generally rely on a single camera, limiting their ability to fully exploit LiDAR-derived depth for photometric alignment and scene colorization. We present Omni-LIVO, a tightly coupled multi-camera LIVO system that leverages multi-view observations to comprehensively utilize LiDAR geometric information across extended spatial regions. Omni-LIVO introduces a Cross-View direct alignment strategy that maintains photometric consistency across non-overlapping views, and extends the Error-State Iterated Kalman Filter (ESIKF) with multi-view updates and adaptive covariance. The system is evaluated on public benchmarks and our custom dataset, showing improved accuracy and robustness over state-of-the-art LIVO, LIO, and visual-inertial SLAM baselines. Code and dataset will be released upon publication. |
| title | Omni-LIVO: Robust RGB-Colored Multi-Camera Visual-Inertial-LiDAR Odometry via Photometric Migration and ESIKF Fusion |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.15673 |