Acta Geodaetica et Cartographica Sinica ›› 2019, Vol. 48 ›› Issue (3): 313-321.doi: 10.11947/j.AGCS.2019.20170716

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

YAN Li1,2, DAI Jicheng1, TAN Junxiang1, LIU Hua1, CHEN Changjun1   

  1. 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)

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

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