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Author: Lu Lezhang
Source丨Computer Vision life
Today, I would like to share with you the results of the Mars Laboratory of the University of Hong Kong. The title is an efficient probabilistic adaptive voxel map for accurate real-time 3D SLAM.
English title: Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online3D SLAM
Paper address: Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online 3D SLAM.
In this paper, an efficient probabilistic adaptive voxel map method is proposed. Considering the measurement error of the laser point and the uncertainty of the laser point caused by the state estimation, the uncertainty of the plane in the map is modeled, and a new method is proposed. An accurate point and surface uncertainty model. This paper analyzes the need for coarse-to-fine voxel maps, and then uses a new type of voxel consisting of hash tables and octrees to efficiently build and update maps. Applying voxel maps to iterative Kalman filtering to construct a maximum posterior probability problem for pose estimation. Experiments on the open KITTI dataset show that the method has high accuracy and efficiency compared to other state-of-the-art methods. The outdoor experiments in the unstructured environment of non-repetitive scanning lidar further verify the adaptability of the mapping method to different environments and lidar scanning modes.
The contributions of this paper are as follows:
1. Aiming at the sparsity and irregularity of lidar point cloud, a size-adaptive, coarse-to-fine voxel construction method is proposed. Adaptive voxel maps are organized in an octree-hash table data structure to improve the efficiency of voxel construction, updating, and querying.
2. This paper proposes an accurate probabilistic voxel map representation that accurately accounts for point uncertainty caused by point measurements and lidar position estimation to model the uncertainty of planes in the map.
3. Real-world applications of the proposed system for laser ranging and mapping are realized and compared with state-of-the-art methods.
Voxel map construction algorithm:
Experimental part:
This method outperforms ICP-based methods and SURFEL-based methods, not only performing well in rotational LiDAR in urban structured environments, but also in solid-state LiDAR in unstructured environments such as parks and mountains good performance.
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