TRLO: An Efficient LiDAR Odometry With 3-D Dynamic Object Tracking and Removal

Simultaneous state estimation and mapping is an essential capability for mobile robots working in dynamic urban environments. The majority of existing simultaneous localization and mapping (SLAM) solutions heavily rely on a primarily static assumption. However, due to the presence of moving vehicles and pedestrians, this assumption does not always hold, leading to localization accuracy decreased and maps distorted. To address this challenge, we propose tracking and removal LiDAR odometry (TRLO), a dynamic LiDAR odometry (LO) that efficiently improves the accuracy of state estimation and generates a cleaner point cloud map. To efficiently detect dynamic objects in the surrounding environment, a deep learning-based method is applied, generating detection bounding boxes. We then design a 3-D multiobject tracker based on an unscented Kalman filter (UKF) and nearest neighbor (NN) strategy to reliably identify and remove dynamic objects. Subsequently, a fast two-stage iterative nearest point solver is employed to solve the state estimation using a cleaned static point cloud. Note that a novel hash-based keyframe database management is proposed for fast access to search keyframes. Furthermore, all the detected object bounding boxes are leveraged to impose posture consistency constraints to further refine the final state estimation. Extensive evaluations and ablation studies conducted on the KITTI, UrbanLoco, and self-collected datasets demonstrate that our approach not only achieves more accurate state estimation but also generates cleaner maps compared with baselines.