Crater identification simulation using LiDAR on Lunar rover

Crater identification is an important task of environment perception, which is benefit for path planning and safe campaign of Lunar rover. In order to overcome the partial missing of point cloud obtained by LiDAR, a three-dimensional data-based crater identification method is proposed to complete the crater by filling in the blind zone for high probability detection, which can be evaluated by completion rate and detection probability of the negative obstacle area in the occupancy grid map, respectively. To obtain a reasonable identification of the craters under various terrain conditions, three parameters related to the terrain and the sensor are varied for evaluating the consequence of crater identification: the terrain frequency, the ranging errors and the angle errors. In simulation experiments, craters with various parameters locate on rolling terrain. The parameters settings used in simulation experiments include the diameter of craters (2 m to 20 m), terrain frequency (>20 m-1), ranging error (<0.07 m) and vertical angle error (<0.3 degrees). The simulation experimental results showed that the proposed method achieves the completion rate of exceeding 84.18 % for providing the complete craters, and the detection probability more than 80.58 % to create the safe occupancy grid map for path planning. This three-dimensional environment perception simulation for crater identification will be instructive for in-situ surface data evaluation and Lunar exploration.

Automated summary

This study proposes a three-dimensional data-based crater identification method to complete the craters by filling in the blind zone of LiDAR point cloud, improving detection accuracy and completion rate. The identification results of craters under different terrain conditions are evaluated by varying parameters related to terrain and the sensor. Simulation experiments show that the proposed method achieves a completion rate exceeding 84.18% and a detection probability of over 80.58% for crater identification.