Camera/LiDAR Sensor Fusion-based Autonomous Navigation

This research presents a novel approach for autonomous navigation of Unmanned Ground Vehicles (UGV) using a camera and LiDAR sensor fusion system. The proposed method is designed to achieve a high rate of obstacle detection, distance estimation, and obstacle avoidance. In order to thoroughly study the form of things and decrease the problem of object occlusion, which frequently happens in camera-based object recognition, the 3D point cloud received from the LiDAR depth sensors is used. The proposed camera and LiDAR sensor fusion design balance the benefits and drawbacks of the two sensors to produce a detection system that is more reliable than others. The UGV's autonomous navigation system is then provided with the region proposal to re-plan its route and navigate appropriately. The experiments were conducted on a UGV system with high obstacle avoidance and fully autonomous navigation capabilities. The outcomes demonstrate that the suggested technique can successfully maneuver the UGV and detect impediments in actual situations.