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LiDAR odometry and localization has attracted increasing research interest inrecent years. In the existing works, iterative closest point (ICP) is widelyused since it is precise and efficient. Due to its non-convexity and its localiterative strategy, however, ICP-based method easily falls into local optima,which in turn calls for a precise initialization. In this paper, we proposeCoFi, a Coarse-to-Fine ICP algorithm for LiDAR localization. Specifically, theproposed algorithm down-samples the input point sets under multiple voxelresolution, and gradually refines the transformation from the coarse point setsto the fine-grained point sets. In addition, we propose a map based LiDARlocalization algorithm that extracts semantic feature points from the LiDARframes and apply CoFi to estimate the pose on an efficient point cloud map.With the help of the Cylinder3D algorithm for LiDAR scan semantic segmentation,the proposed CoFi localization algorithm demonstrates the state-of-the-artperformance on the KITTI odometry benchmark, with significant improvement overthe literature.

CoFi: Coarse-to-Fine ICP for LiDAR Localization in an Efficient Long-lasting Point Cloud Map