Autonomous Obstacle Detection and Avoidance of Lunar Landing Based on Active and Passive Sensors

lunar landing and exploration in the future require precise landing near the living cabin, as well as some complex scientific target environments such as moon craters and caves, requiring high positioning and navigation accuracy, and these complex areas is rugged, there is no prior environmental information. In view of the characteristics of the lunar surface, an obstacle detection and site selection method based on illumination gradient and 3D point cloud is proposed. When the distance from the lunar surface is relatively far, the illumination direction of image acquisition is determined; the region of interest (ROI) is selected to improve detection efficiency; then detect the rocks and craters in each ROI, and output them as fitted ellipses with geometric and position information, construct a mask to remove obstacles, and generate a safe landing zone. When the distance from the lunar surface is relatively close. Perform motion compensation on the point cloud data detected by the lidar, correct it according to the local gravity direction, and perform voxel grid down-sampling, using morphological progressive filtering and random sampling consistency for plane fitting and external point obstacle collection, extract obstacles in safe flat areas. In the simulated lunar landing test site, the UAV platform equipped with active and passive sensors was used. Based on the principles of physical kinematics and dynamics, the reduction simulation of the obstacle avoidance landing process of lunar descent was carried out to verify the algorithm. The method presented in this paper is able to detect and landing accurately in a safe area in real time, which is shown in the test results.