3D LiDAR-based obstacle detection and tracking for autonomous navigation in dynamic environments

An accurate perception with a rapid response is fundamental for any autonomous vehicle to navigate safely. Light detection and ranging (LiDAR) sensors provide an accurate estimation of the surroundings in the form of 3D point clouds. Autonomous vehicles use LiDAR to realize obstacles in the surroundings and feed the information to the control units that guarantee collision avoidance and motion planning. In this work, we propose an obstacle estimation (i.e., detection and tracking) approach for autonomous vehicles or robots that carry a three-dimensional (3D) LiDAR and an inertial measurement unit to navigate in dynamic environments. The success of u-depth and restricted v-depth maps, computed from depth images, for obstacle estimation in the existing literature, influences us to explore the same techniques with LiDAR point clouds. Therefore, the proposed system computes u-depth and restricted v-depth representations from point clouds captured with the 3D LiDAR and estimates long-range obstacles using these multiple depth representations. Obstacle estimation using the proposed u-depth and restricted v-depth representations removes the requirement for some of the high computation modules (e.g., ground plane segmentation and 3D clustering) in the existing obstacle detection approaches from 3D LiDAR point clouds. We track all static and dynamic obstacles until they are on the frontal side of the autonomous vehicle and may create obstructions in the movement. We evaluate the performance of the proposed system on multiple open data sets of ground and aerial vehicles and self-captured simulated data sets. We also evaluate the performance of the proposed system with real-time captured data using ground robots. The proposed method is faster than the state-of-the-art (SoA) methods, though the performance of the proposed method is comparable with the SoA methods in terms of dynamic obstacle detection and estimation of their states.