Omni-LIVO: Robust RGB-Colored Multi-Camera Visual-Inertial-LiDAR Odometry via Photometric Migration and ESIKF Fusion
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.
Fields of Study & Subjects
Robotics and Sensor-Based Localization
3D Surveying and Cultural Heritage
Remote Sensing and LiDAR Applications
Publication details
- Journal / source
- IEEE Robotics and Automation Letters
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 11
- Issue
- 4
- Pages
- 4369–4376
- Publication date
- 9 February 2026
- Language
- EN