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To achieve accurate and robust pose estimation in Simultaneous Localizationand Mapping (SLAM) task, multi-sensor fusion is proven to be an effectivesolution and thus provides great potential in robotic applications. This paperproposes FAST-LIVO, a fast LiDAR-Inertial-Visual Odometry system, which buildson two tightly-coupled and direct odometry subsystems: a VIO subsystem and aLIO subsystem. The LIO subsystem registers raw points (instead of featurepoints on e.g., edges or planes) of a new scan to an incrementally-built pointcloud map. The map points are additionally attached with image patches, whichare then used in the VIO subsystem to align a new image by minimizing thedirect photometric errors without extracting any visual features (e.g., ORB orFAST corner features). To further improve the VIO robustness and accuracy, anovel outlier rejection method is proposed to reject unstable map points thatlie on edges or are occluded in the image view. Experiments on both open datasequences and our customized device data are conducted. The results show ourproposed system outperforms other counterparts and can handle challengingenvironments at reduced computation cost. The system supports both multi-linespinning LiDARs and emerging solid-state LiDARs with completely differentscanning patterns, and can run in real-time on both Intel and ARM processors.We open source our code and dataset of this work on Github to benefit therobotics community.

FAST-LIVO: Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry