Semantic Geometric Fusion Multi-object Tracking and Lidar Odometry in Dynamic Environment

Since point clouds obtained from lidar are spatially discrete and non-repetitive, directly using point clouds to achieve object data association and robust state estimation is not a simple task. Further, tracking and analyzing the object states facilitates determining how they are involved in localization and mapping works. In this paper, we propose a least-squares estimator incorporating semantic 3D bounding boxes and geometric point clouds to achieve accurate and robust tracking of multiple objects. Then, the proposed tracker is integrated into a multi-object lidar odometry (MLO) system using only point clouds as input. By analyzing object motion states, the mapping module uses static objects and environmental features to eliminate accumulated errors. Meanwhile, the MLO system provides continuous object trajectories in map coordinate. Finally, we evaluate the effectiveness of the proposed semantic geometric fusion multi-object tracking (SGF-MOT) module and the localization accuracy of the MLO system under the public KITTI dataset.