Semantic geometric fusion multi-object tracking and lidar odometry in dynamic environment

Simultaneous localization and mapping systems based on rigid scene assumptions cannot achieve reliable positioning and mapping in a complex environment with many moving objects. To solve this problem, this paper proposes a novel dynamic multi-object lidar odometry (MLO) system based on semantic object recognition technology. The proposed system enables the reliable localization of robots and semantic objects and the generation of long-term static maps in complex dynamic scenes. For ego-motion estimation, the proposed system extracts environmental features that take into account both semantic and geometric consistency constraints. Then, the filtered features can be robust to the semantic movable and unknown dynamic objects. In addition, we propose a new least-squares estimator that uses geometric object points and semantic box planes to realize the multi-object tracking (SGF-MOT) task robustly and precisely. In the mapping module, we implement dynamic semantic object detection using the absolute trajectory tracking list. By using static semantic objects and environmental features, the system eliminates accumulated localization errors and produces a purely static map. Experiments on the public KITTI dataset show that the proposed MLO system provides more accurate and robust object tracking performance and better real-time localization accuracy in complex scenes compared to existing technologies.