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

• URL: http://arxiv.org/abs/2110.10194v2
• Date: Wed, 28 Feb 2024 07:21:50 GMT
• Title: CoFi: Coarse-to-Fine ICP for LiDAR Localization in an Efficient Long-lasting Point Cloud Map
• Authors: Yecheng Lyu, Xinming Huang, Ziming Zhang
• Abstract summary: In this paper, we propose an algorithm that transforms the point sets into fine-grained point sets. We also propose a map based LiDAR localization algorithm that extracts semantic points from the frames and apply CoFi to estimate the pose. With help of the proposed CoFi, the proposed CoFi demonstrates the Cylinder3D algorithm for semantic segmentation.
• Score: 21.20066952518376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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