Simultaneous Pose Estimation and Velocity Estimation of an Ego Vehicle and Moving Obstacles Using LiDAR Information Only

It is important to accurately obtain the motion information of the ego vehicle and surrounding vehicles for autonomous vehicles to plan safe trajectories in complicated traffic environments. In this paper, a LiDAR-based estimation method is developed to simultaneously identify the pose and the velocity information of an ego vehicle and its surrounding moving obstacles. Specifically, a pose estimation network is designed to estimate the poses of both the ego vehicle and the obstacles only with the continuous point clouds obtained by the LiDAR mounted on the ego vehicle. In the network, PointNet++ is utilized as the backbone to extract point-wise features and divide the points into the static part and the moving part. The former is used to estimate the ego vehicle's pose, while the latter is applied for the obstacle pose identification. Then, a reduced-order observer is designed to estimate the velocities, whose convergence is proved with the Lyapunov theory. Finally, both simulation and experiment results are provided to show the effectiveness of the proposed method.