Unmanned Surface Vehicle Target Detection Based on LiDAR

In recent years, target detection technology has been a hot and challenging topic in the field of computer vision. Due to the complexity and uncontrollability of the water environment, research on unmanned surface vehicle target detection is relatively weak, attracting increasing attention. Laser radar sensors offer advantages such as accurate distance measurement, strong anti-interference capabilities, and high measurement accuracy for water surface target detection. This paper proposes a method for unmanned surface vehicle water surface obstacle detection based on laser radar. First, the laser radar point cloud data is visualized and preprocessed. The point cloud preprocessing mainly involves downsampling the point cloud to significantly reduce the data volume, thereby improving the efficiency of subsequent algorithms. Next, based on the features of the water surface obstacle point cloud, point cloud segmentation is performed to filter out the water surface points and retain only the obstacle points. Finally, the obstacle point cloud, which has been transformed into a grid representation, is clustered using the DBSCAN algorithm to achieve target clustering.