Global fine registration of point cloud in LiDAR SLAM based on pose graph

被引：0
|
作者：
Yan L. [1 ,2 ]
Dai J. [1 ]
Tan J. [1 ]
Liu H. [1 ]
Chen C. [1 ]
机构：
[1] School of Geodesy and Geomatics, Wuhan University, Wuhan
[2] State Key Laboratory of Information Engineering in Surveying, Wuhan University, Wuhan
来源：
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2019年 / 48卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Global optimization; Graph optimization; Iterative closest point; Point cloud refine; Simultaneous localization and mapping;
D O I：
10.11947/j.AGCS.2019.20170716
中图分类号：
学科分类号：
摘要：
The laser scanning system based on simultaneous localization and mapping(SLAM) technology has the advantages of low cost and high efficiency. It has drawn wide attention in the field of surveying and mapping in recent years. Although real-time data acquisition can be achieved using SLAM technology, the precision of the data can't be ensured, and inconsistency exists in the acquired point cloud. In order to improve the precision of the point cloud obtained by this kind of system, this paper presents a hierarchical point cloud global optimization algorithm. Firstly, the "point-to-plane" Iterative closest point algorithm (ICP) is used to match the overlapping point clouds to form constraints between the trajectories of the scanning system. Then a pose graph is constructed to optimize the trajectory. Finally, the optimized trajectory is used to refine the point cloud. The computational efficiency is improved by decomposing the optimization process into two levels, i.e. local level and global level. The experimental results show that the RMSE of the distance between the corresponding points in overlapping areas is reduced by about 50% after optimization, and the internal inconsistency is effectively eliminated. © 2019, Surveying and Mapping Press. All right reserved.
引用
收藏
页码：313 / 321
页数：8
相关论文
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