针对移动机器人在未知环境感知过程中对三维点云快速聚类分割的需求，提出一种基于图像信息约束的三维激光点云聚类方法。首先通过点云预处理获取有效的三维环境信息，采用RANSAC方法进行地面点云的分割剔除。其次传感器数据在完成时空配准后引入YOLOv5目标检测算法，对三维点云K-means聚类算法进行改进，利用二维图像目标物的检测框范围约束三维点云，减少非目标物的干扰；基于图像检测信息实现点云聚类算法的参数初始化；采用类内异点剔除法优化聚类结果。最后搭建移动机器人硬件平台，对箱体进行测试，实验结果表明，本文方法的聚类准确率和聚类时间分别为86.96%和23 ms，可用于移动机器人导航避障、自主搬运等领域。

After testing with 50 frames of random data, the experimental results show that the clustering accuracy and clustering time of this method are 86.96% and 23 ms, respectively, which are better than other algorithms, and can be used in mobile robot navigation and obstacle avoidance, autonomous handling, and other fields.Aiming at the requirement of fast clustering and segmentation of 3D point clouds for mobile robots in the process of perception of unknown environments, a 3D laser point cloud clustering method based on image information constraints is proposed. Firstly, the effective 3D environment information is obtained through point cloud preprocessing, and the RANSAC method is used to segment and eliminate the ground point cloud. Secondly, the sensor data is introduced into the YOLOv5 target detection algorithm after completing the spatiotemporal registration, and the K-means clustering algorithm of the 3D point cloud is improved. The detection frame range of the 2D image target is used to constrain the 3D point cloud and reduce the interference of non-target objects. The parameter initialization of the point cloud clustering algorithm is realized based on the image detection information. The clustering results are optimized by the intra-class outlier elimination method. Finally, the mobile robot hardware platform is built, and the box is tested. The experimental results show that the clustering accuracy and clustering time of the method in this paper are 86.96% and 23 ms, respectively, which can be used in mobile robot navigation and obstacle avoidance, autonomous handling, and other fields.