LAUSR

With the widespread use of UAVs in daily life, there are many sensors and algorithms used to ensure flight safety. Among these sensors, lidar has been gradually applied to UAVs due to its stability and portability. However, in the actual application, lidar changes its position with the movement of the UAV, resulting in an offset in the detected point cloud. What’s more, when the lidar works, it scatters laser light from the center to the surroundings, which causes the detected point cloud to be externally sparse and dense inside. This point cloud with uneven density is difficult to cluster using common clustering algorithms. In this paper, a velocity estimation method based on the polynomial fit is used to estimate the position of the lidar as it scans each point and then corrects the twisted point cloud. Besides, the clustering algorithm based on relative distance and density (CBRDD) is used to cluster the point cloud with uneven density. To prove the effectiveness of the obstacle detection method, the simulation experiment and actual experiment were carried out. The results show that the method has a good effect on obstacle detection.