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arXiv · 2509.15673

Omni-LIVO: Robust RGB-Colored Multi-Camera Visual-Inertial-LiDAR Odometry via Photometric Migration and ESIKF Fusion

Yinong Cao·Chenyang Zhang·Xin He·Yuwei Chen·Chengyu Pu·Bingtao Wang·Kaile Wu·Shouzheng Zhu·Fei Han·Shijie Liu·Chunlai Li·Jianyu Wang
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Abstract

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.

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Yinong Cao, Chenyang Zhang, Xin He, Yuwei Chen, Chengyu Pu, Bingtao Wang, Kaile Wu, Shouzheng Zhu, Fei Han, Shijie Liu, Chunlai Li, Jianyu Wang. 2025-09-19. Omni-LIVO: Robust RGB-Colored Multi-Camera Visual-Inertial-LiDAR Odometry via Photometric Migration and ESIKF Fusion. https://doi.org/10.1109/lra.2026.3662590

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