SLAM激光点云整体精配准位姿图技术

doi:10.11947/j.AGCS.2019.20170716

测绘学报 ›› 2019, Vol. 48 ›› Issue (3): 313-321.doi: 10.11947/j.AGCS.2019.20170716

SLAM激光点云整体精配准位姿图技术

闫利^1,2, 戴集成¹, 谭骏祥¹, 刘华¹, 陈长军¹

1. 武汉大学测绘学院, 湖北武汉 430079;
2. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079

收稿日期:2017-12-19 修回日期:2018-10-12 出版日期:2019-03-20 发布日期:2019-04-10
通讯作者: 陈长军 E-mail:chencj@whu.edu.cn
作者简介:闫利(1966-),男,教授,博士生导师,研究方向为摄影测量、遥感和三维激光扫描技术。E-mail:lyan@sgg.whu.edu.cn
基金资助:
国家自然科学基金（41771486）

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

YAN Li^1,2, DAI Jicheng¹, TAN Junxiang¹, LIU Hua¹, CHEN Changjun¹

1. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;
2. State Key Laboratory of Information Engineering in Surveying, Wuhan University, Wuhan 430079, China

Received:2017-12-19 Revised:2018-10-12 Online:2019-03-20 Published:2019-04-10
Supported by:
The National Natural Science Foundation of China (No. 41771486)

摘要/Abstract

摘要： 基于同步定位与制图（simultaneous localization and mapping，SLAM）技术的激光扫描系统具有成本低、效率高的优点，近年来在测绘领域得到了广泛关注。虽然基于SLAM技术的激光扫描系统能够实现实时数据获取，但该数据获取方式难以保证点云精度，不同位置获取的同一地物的点云存在位置不一致。为了提高该类系统所获点云精度，本文提出一种分层次点云全局优化方法。该方法首先通过“点-切平面”迭代最近邻算法对重叠点云进行配准，形成扫描系统轨迹间的约束；然后构建位姿图对轨迹进行优化，利用优化后的轨迹对点云进行修正。算法通过将优化过程分解为局部和整体两个层次以提高计算效率。试验结果表明，优化后点云同名点对间的距离中误差减小约50%，内部不一致现象得到有效消除。

关键词: 点云修正, 同步定位与制图, 全局优化, 图优化, 迭代最近点法

Abstract: 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.

Key words: point cloud refine, simultaneous localization and mapping, global optimization, graph optimization, iterative closest point

中图分类号:

P232

闫利, 戴集成, 谭骏祥, 刘华, 陈长军. SLAM激光点云整体精配准位姿图技术[J]. 测绘学报, 2019, 48(3): 313-321.

YAN Li, DAI Jicheng, TAN Junxiang, LIU Hua, CHEN Changjun. Global fine registration of point cloud in LiDAR SLAM based on pose graph[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(3): 313-321.

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SLAM激光点云整体精配准位姿图技术

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

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