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.

翻译：宽视场（FoV）LiDAR传感器能够在广阔环境中提供密集的几何信息，但现有的LiDAR-惯性-视觉里程计（LIVO）系统通常依赖单一相机，限制了其充分利用LiDAR深度信息进行光度对齐与场景着色的能力。本文提出Omni-LIVO，一种紧耦合的多相机LIVO系统，通过多视角观测全面利用大范围空间区域的LiDAR几何信息。Omni-LIVO引入了跨视角直接对齐策略，以保持非重叠视角间的光度一致性，并通过多视角更新与自适应协方差扩展了误差状态迭代卡尔曼滤波器（ESIKF）。该系统在公开基准数据集及我们自建数据集上进行了评估，结果表明其在精度与鲁棒性上优于当前最先进的LIVO、LIO及视觉-惯性SLAM基线方法。代码与数据集将在论文发表时公开。