激光雷达
束流调整
计算机科学
稳健性(进化)
比例(比率)
点云
图形
黑森矩阵
人工智能
数据挖掘
遥感
数学
理论计算机科学
地图学
地理
图像(数学)
生物化学
化学
应用数学
基因
作者
Xiyuan Liu,Zheng Liu,Fanze Kong,Fu Zhang
出处
期刊:IEEE robotics and automation letters
日期:2023-03-01
卷期号:8 (3): 1523-1530
被引量:7
标识
DOI:10.1109/lra.2023.3238902
摘要
Reconstructing an accurate and consistent large-scale LiDAR point cloud map is crucial for robotics applications. The existing solution, pose graph optimization, though it is time-efficient, does not directly optimize the mapping consistency. LiDAR bundle adjustment (BA) has been recently proposed to resolve this issue; however, it is too time-consuming on large-scale maps. To mitigate this problem, this paper presents a globally consistent and efficient mapping method suitable for large-scale maps. Our proposed work consists of a bottom-up hierarchical BA and a top-down pose graph optimization, which combines the advantages of both methods. With the hierarchical design, we solve multiple BA problems with a much smaller Hessian matrix size than the original BA; with the pose graph optimization, we smoothly and efficiently update the LiDAR poses. The effectiveness and robustness of our proposed approach have been validated on multiple spatially and timely large-scale public spinning LiDAR datasets, i.e., KITTI, MulRan and Newer College, and self-collected solid-state LiDAR datasets under structured and unstructured scenes. With proper setups, we demonstrate our work could generate a globally consistent map with around 12 $\%$ of the sequence time.
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