Abstract: 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.