A Fast Obstacle Detection Algorithm Based on 3D LiDAR and Multiple Depth Cameras for Unmanned Ground Vehicles

With the advancement of technology, unmanned ground vehicles (UGVs) have shown increasing application value in various tasks, such as food delivery and cleaning. A key capability of UGVs is obstacle detection, which is essential for avoiding collisions during movement. Current mainstream methods use point cloud information from onboard sensors, such as light detection and ranging (LiDAR) and depth cameras, for obstacle perception. However, the substantial volume of point clouds generated by these sensors, coupled with the presence of noise, poses significant challenges for efficient obstacle detection. Therefore, this paper presents a fast obstacle detection algorithm designed to ensure the safe operation of UGVs. Building on multi-sensor point cloud fusion, an efficient ground segmentation algorithm based on multi-plane fitting and plane combination is proposed in order to prevent them from being considered as obstacles. Additionally, instead of point cloud clustering, a vertical projection method is used to count the distribution of the potential obstacle points through converting the point cloud to a 2D polar coordinate system. Points in the fan-shaped area with a density lower than a certain threshold will be considered as noise. To verify the effectiveness of the proposed algorithm, a cleaning UGV equipped with one LiDAR sensor and four depth cameras is used to test the performance of obstacle detection in various environments. Several experiments have demonstrated the effectiveness and real-time capability of the proposed algorithm. The experimental results show that the proposed algorithm achieves an over 90% detection rate within a 20 m sensing area and has an average processing time of just 14.1 ms per frame.