DL-SLOT: Tightly-Coupled Dynamic LiDAR SLAM and 3D Object Tracking Based on Collaborative Graph Optimization

Ego-pose estimation and 3D object tracking are two critical problems that autonomous driving systems must solve. The solutions to these problems are usually based on their respective assumptions, i.e., the static world assumption for simultaneous localization and mapping (SLAM) and the accurate ego-pose assumption in object tracking. However, these assumptions may not hold in dynamic road scenarios, where SLAM and object tracking are closely correlated. Therefore, we propose DL-SLOT, a tightly-coupled dynamic LiDAR SLAM and 3D object tracking method that addresses both problems simultaneously. This method integrates the state estimations of both the autonomous vehicle and the stationary and dynamic objects in the environment in a unified optimization framework. First, we use object detection to identify all points belonging to potentially dynamic objects. Subsequently, LiDAR odometry is performed using the filtered point cloud. Simultaneously, we propose a sliding window-based 3D multi-object tracking method considering the historical trajectories of the tracked objects. The states of the ego-vehicle, stationary objects, and dynamic objects are jointly estimated by the sliding window-based collaborative graph optimization to achieve SLOT. True stationary objects are restored from the potentially dynamic object set to join the optimization. Finally, a global pose graph is implemented to eliminate the accumulated error. Experiments on the KITTI datasets demonstrate that our method achieves better accuracy than SLAM and 3D object tracking baseline methods. This confirms that solving SLAM and object tracking simultaneously is mutually beneficial, and dramatically improves the robustness and accuracy of SLAM and object tracking in dynamic road scenarios.