基于点云深度信息的有效点云阈值自适应方法  

Effective point cloud threshold adaptive method based on depthinformation of point cloud

作　　者：慕志洋 周伟[1] 张林 范浩 袁婷萱 MU Zhiyang;ZHOU Wei;ZHANG Lin;FAN Hao;YUAN Tingxuan(College of Nuclear Technology and Automation Engineering,Chengdu University of Technology,Chengdu 610059,China)

机构地区：[1]成都理工大学核技术与自动化工程学院,四川成都610059

出　　处：《测绘通报》2024年第12期11-17,共7页Bulletin of Surveying and Mapping

基　　金：四川省科技计划重点研发项目(2021YFG0075)。

摘　　要：复杂环境中实时定位与地图构建(SLAM)是机器人自动导航研究领域的难点之一。机器人所处周边空间环境的剧烈变化易使SLAM构图出现漂移和重影,降低构图的精度。为此本文提出了一种有效点云阈值的自适应优化方法,以提高SLAM算法在复杂环境的适用性。该算法通过实时计算激光点云的三维数据得到点云的深度信息,并依据深度信息的波动性和点云分布的离散系数自适应优化有效点云阈值,从而实现闭环控制。试验表明,本文阈值自适应优化方法明显改善了快速且直接的激光雷达与惯性里程计算法在复杂环境中的构图效果,矫正了该算法在狭窄环境中的里程计坐标误差,并将回环定位误差降低了7.5%。SLAM in complex environments is one of the challenging tasks in the field of robot autonomous navigation research.The drastic changes in the surrounding spatial environment can lead to drift and overlap in SLAM mapping,thereby reducing mapping accuracy.To address this issue,this paper proposes an effective adaptive optimization method for point cloud thresholding,improving the applicability of SLAM algorithms in complex environments.The algorithm calculates the depth information of the point cloud in real-time and adaptively optimizes the effective point cloud threshold based on the fluctuation of depth information and the coefficient of variation of point cloud distribution,thereby achieving closed-loop control.Experiments show that the proposed threshold adaptive optimization method significantly improves the mapping performance of fast and direct LiDAR with inertial odometry algorithms in complex environments.It corrects the odometer coordinate errors of this algorithm in narrow environments and reduces loop closure positioning errors by 7.5%.

关 键 词：实时定位与地图构建 深度信息 离散系数 阈值自适应 点云 

分 类 号：P237[天文地球—摄影测量与遥感]