LiDAR odometry and localization has attracted increasing research interest in recent years. In the existing works, iterative closest point (ICP) is widely used since it is precise and efficient. Due to its non-convexity and its local iterative strategy, however, ICP-based method easily falls into local optima, which in turn calls for a precise initialization. In this paper, we propose CoFi, a Coarse-to-Fine ICP algorithm for LiDAR localization. Specifically, the proposed algorithm down-samples the input point sets under multiple voxel resolution, and gradually refines the transformation from the coarse point sets to the fine-grained point sets. In addition, we propose a map based LiDAR localization algorithm that extracts semantic feature points from the LiDAR frames 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-art performance on the KITTI odometry benchmark, with significant improvement over the literature.

翻译：摘要：近年来，激光雷达里程计与定位技术引起了日益增长的学术研究兴趣。在现有工作中，迭代最近点算法(ICP)因其精确性和高效性被广泛使用。然而，由于ICP的非凸性及其局部迭代策略，基于ICP的方法容易陷入局部最优，这反过来要求精确的初始位姿。本文提出了一种用于激光雷达定位的粗到细ICP算法CoFi。具体而言，该算法在多体素分辨率下对输入点集进行降采样，并逐步将变换从粗点集细化到精细点集。此外，我们提出了一种基于地图的激光雷达定位算法，从激光雷达帧中提取语义特征点，并应用CoFi在高效点云地图上估计位姿。借助用于激光雷达扫描语义分割的Cylinder3D算法，所提出的CoFi定位算法在KITTI里程计基准测试中展现出最先进的性能，相较于现有文献有显著提升。