多相机融合激光雷达与惯性数据，提升大范围环境定位精度与鲁棒性。
Omni-LIVO: Robust RGB-Colored Multi-Camera Visual-Inertial-LiDAR Odometry via Photometric Migration and ESIKF Fusion
- 跨视角直接对齐，实现非重叠视图间的光照一致性保持。
- 改进的误差状态迭代卡尔曼滤波支持多视角更新与自适应协方差。
- 在公开与自建数据集上均优于当前最优的视觉-惯性-激光里程计系统。
广角激光雷达传感器可在大范围内提供密集几何信息，但现有激光雷达-惯性-视觉里程计（LIVO）系统通常仅依赖单个摄像头，限制了其对激光雷达深度信息的充分利用。本文提出Omni-LIVO，一种紧密耦合的多相机LIVO系统，通过多视角观测全面挖掘激光雷达在广阔空间中的几何信息。该系统引入跨视角直接对齐策略，保持非重叠视图间的光度一致性，并将误差状态迭代卡尔曼滤波（ESIKF）扩展为支持多视角更新与自适应协方差。在公开基准和自建数据集上的评估表明，Omni-LIVO在精度与鲁棒性上均显著优于当前最先进的LIVO、LIO及视觉-惯性SLAM方法。代码与数据集将在论文发表后公开。
原文摘要 · Abstract (English)
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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