基于激光雷达数据的火星表面障碍物识别

Obstacle recognition on Mars surface based on LiDAR data

火星车的环境感知能力是其进行智能移动和探测的基础,而障碍物检测识别是环境感知中的一个重要方面,识别效果直接决定了火星车工作能力和安全性。本文提出一种基于激光雷达数据的火星表面障碍物自动识别方法。通过获取的激光雷达点云数据,首先在分析激光反射强度理论的基础上,通过强度补偿理论将点云强度根据距离、角度因素进行修正,进而构建激光雷达强度值与目标特征的反射关系。通过大津法自动求取全局阈值,自适应的将火星表面点云分类为障碍物点云和非障碍物点云;然后通过曲率约束剔除不符合条件的障碍物点云;最后利用基于八叉树叶节点的连通性聚类,实现火星表面障碍物点云的识别。模拟实验结果表明,该方法可实现激光雷达点云中的火星表面障碍物有效提取,典型障碍物识别精度接近90%,为基于火星车障碍物检测和环境感知相关研究提供借鉴。

The environment perception ability of the Mars rover is the basis of its intelligent movement and detection.Obstacle detection is an important aspect of environment perception,which directly determines the working ability and safety of the Mars rover.In this paper,a method of identifying obstacles on the surface of Mars based on LiDAR data is proposed.Based on the obtained LiDAR point cloud data,the intensity of the point cloud is modified according to the distance and angle factors through the intensity compensation theory based on the analysis of the laser reflection intensity theory,and then the reflection relationship between the lidar intensity value and the target characteristics is constructed.The global threshold is automatically obtained through the Otsu method,and the Mars surface point cloud is adaptively classified into an obstacle point cloud and a non-obstacle point cloud.Then,the obstacle point cloud which does not meet the conditions is removed by curvature constraint.Finally,using the connectivity clustering based on Octree-based leaf nodes,the recognition of the obstacle point cloud on the surface of Mars is realized.Through the simulation experiment,the results show that this method can effectively extract the obstacles on the surface of Mars from the LiDAR point cloud,and provide a reference for the related research based on the obstacle monitoring of the Mars rover and environmental perception.