基于欧氏聚类的三维激光点云田间障碍物检测方法

Field Obstacle Detection Method of 3D LiDAR Point Cloud Based on Euclidean Clustering

为满足目前农业机械(简称农机)自动驾驶中农田障碍物检测的需求,提出了一种使用三维激光雷达检测田间障碍物的方法。该方法首先对采集的环境点云进行预处理,采用体素栅格下采样滤波,将稠密的点云在不损失特征信息的情况下进行减量;采用三维长方体对角点划定感兴趣区域以便快速计算;采用随机采样一致性(RANSAC)算法检测出农田地面点云,将地面点云与地面上障碍物点云进行分割。然后对地面上障碍物点云基于K维树(K-d tree)进行欧氏聚类,其中聚类的距离阈值为0.6 m。最后判断聚类的点数量和外接长方体体积,过滤掉点数和体积过大或过小的无效聚类从而得出障碍物。应用32线激光雷达在北京市小汤山国家精准农业示范基地采集田间环境点云,分别对田间机具、草堆、田埂、地头矮房、路边树木和田间行人进行检测,结果表明该方法对田间常见障碍物有较好的检测效果。考虑到人是田间行车安全的重要因素,在田间进行了行人横穿于雷达视野前方且与雷达距离分别为5、10、15、20、25、30 m时算法的检测效果试验,试验结果表明田间行人在30 m内平均检出率为96.11%。该方法可用于大田环境下障碍物的检测,为农机自主行走过程中的避障策略研究提供了基础。

In response to the current needs of farmland obstacle detection in the automatic driving of agricultural machinery,a method of using three-dimensional LiDAR to detect field obstacles was proposed.Firstly,the collected environmental point cloud was preprocessed.The voxel grid down-sampling method was used to filter the dense point cloud without losing feature information.A bounding box was used to segment the region of interest for fast calculation.The random sample consensus algorithm(RANSAC)was used to detect the farmland ground,and the ground point cloud was removed from the whole point data so that the obstacle points were extracted.Then the obstacle point cloud was clustered by Euclidean distance based on the K-d tree,and the distance threshold of clustering was 0.6 m in this test.Finally,the size of the cluster and the volume of the circumscribed cuboid were judged,and invalid clusters that were too large or too small were filtered out to obtain obstacles.A LiDAR with 32 channels was used to collect field obstacle point cloud at National Experiment Station for Precision Agriculture in Beijing Xiaotangshan.The algorithm was used to detect agricultural implement,haystack,field ridge,low houses,roadside trees,and field pedestrian.The test showed that the algorithm was suitable for the field common obstacles detection.When detecting pedestrians in the field,the people crossed the front view of the LiDAR and the distances from the LiDAR respectively were 5 m,10 m,15 m,20 m,25 m and 30 m to test the effect of the algorithm at different distances.The results showed that the average detection rate of dynamically walking people in the field within 30 m was 96.11%.This algorithm can be used to detect obstacles in the field environment and can provide a basis for the research of obstacle avoidance strategies in agricultural machinery autonomous driving.