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Yuan, Chongjian; Xu, Wei; Liu, Xiyuan; Hong, Xiaoping; Zhang, Fu
IEEE robotics & automation letters, 07/2022, Letnik: 7, Številka: 3Journal Article
This letter proposes an efficient and probabilistic adaptive voxel mapping method for LiDAR odometry. The map is a collection of voxels; each contains one plane feature that enables the probabilistic representation of the environment and accurate registration of a new LiDAR scan. We further analyze the need for coarse-to-fine voxel mapping and then use a novel voxel map organized by a Hash table and octrees to build and update the map efficiently. We apply the proposed voxel map to an iterated extended Kalman filter and construct a maximum a posteriori probability problem for pose estimation. Experiments on the open KITTI dataset show the high accuracy and efficiency of our method compared to other state-of-the-art methods. Experiments on indoor and unstructured outdoor environments with solid-state LiDAR and non-repetitive scanning LiDAR further verify the adaptability of our mapping method to different environments and LiDAR scanning patterns (see our attached video 1 ). Our codes and dataset are open-sourced on Github 2
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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