测绘通报 ›› 2024, Vol. 0 ›› Issue (11): 49-55.doi: 10.13474/j.cnki.11-2246.2024.1109

• 学术研究 • 上一篇    下一篇

复杂城区环境下基于POS的车载激光点云纠正

韩萱, 刘如飞, 崔健慧, 王旻烨, 李泽宇   

  1. 山东科技大学测绘与空间信息学院, 山东 青岛 266590
  • 收稿日期:2024-02-04 发布日期:2024-12-05
  • 通讯作者: 崔健慧,E-mail:jhcui@sdust.edu.cn
  • 作者简介:韩萱(1999-),女,硕士生,研究方向为组合导航。E-mail:sdkjdxhx@163.com
  • 基金资助:
    山东省自然科学基金(ZR2021QD131);国家自然科学基金(42301519)

Correction of vehicle-borne laser point clouds in complex urban environments based on POS

HAN Xuan, LIU Rufei, CUI Jianhui, WANG Minye, LI Zeyu   

  1. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
  • Received:2024-02-04 Published:2024-12-05

摘要: 在高楼林立、树木密集的城区环境下,GNSS信号易受遮挡,GNSS/INS组成的POS位姿误差迅速累积,因此基于POS提供的位姿信息解算出的车载激光点云位置误差增大,从而出现局部非刚性形变现象。为解决上述问题,本文在传统基于POS的车载激光点云纠正方法的基础上,首先针对复杂城区环境下易出现的GNSS信号遮挡及车辆运行情况,通过不同控制点间距对比,制定最佳控制点布设方案;然后深入分析不同GNSS信号遮挡条件下POS的误差特性,构建合理的误差时变插值模型;最后利用控制点误差插值信息对POS位姿进行修正,从而为车载激光点云解算提供可靠的位姿信息。试验结果表明,采用本文策略对POS进行修正,其定位精度相较于修正前可提升约62.50%;利用修正后的POS位姿解算出的车载激光点云,其精度相较于点云纠正前可提升约75.45%。

关键词: 复杂城区环境, POS, 误差插值, 车载激光点云, 点云纠正

Abstract: In the urban environment with tall buildings and dense trees, the GNSS signals are susceptible to occlusion, and the position error of the POS composed of GNSS/INS accumulates rapidly. This leads to an increase in the coordinate errors of vehicle-borne laser point clouds computed from the position attitude data provided by the POS. Additionally, the point cloud shows localized non-rigid deformation phenomena. To solve the above problems, based on the traditional POS-based point cloud correction method, this paper further studies the complex urban environment with GNSS signal occlusion and vehicle operation conditions. Through comparing the spacing of different control points, it formulates the optimal deployment scheme for control points. The error characteristics of POS under different GNSS signal occlusion conditions are deeply analyzed so as to construct a reasonable error time-varying interpolation model. The control point error interpolation information is utilized to correct the POS position to provide reliable position information for the vehicle-borne laser point cloud solving. The experimental results show that the positioning accuracy of the POS corrected using the strategy of this paper can be improved by about 62.50% compared with the pre-correction one. The accuracy of the corrected point cloud solved using the corrected POS position can be improved by about 75.45% compared with the pre-point cloud correction one.

Key words: urban complex environment, POS, error interpolation, vehicle-borne laser point cloud, point cloud correction

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