ELPTNet: An Efficient LiDAR-based 3D Pedestrian Tracking Network for Autonomous Navigation Social Robots

Abstract

Autonomous navigation social robots need to track pedestrian movements in real-time with high precision to optimize path planning and avoid collisions. However, the main challenge of pedestrian tracking lies in the significant variations in human posture, which differ from rigid-body structures like vehicles. In this paper, we propose an Efficient LiDAR-based 3D Pedestrian Tracking Network (ELPTNet). First, our ELPTNet employs a 3D object detector to extract directional 3D pedestrian bounding boxes from LiDAR point clouds. Then, our ELPTNet employs a Constant Acceleration (CA) model and prediction confidence for target trajectory prediction. During the data association process, it integrates geometric, appearance, and motion features to enhance the robustness and real-time performance of 3D MOT when targets are temporarily occluded. Experimental results demonstrate that our ELPTNet achieves the highest ranking on the large-scale JRDB dataset for the 3D tracking task, outperforming previous state-of-the-art (SOTA) methods with improvements of 8.4% in MOTA and 6.6% in HOTA. Additionally, our ELPTNet attains an inference speed of 61 frames per second (FPS) on a single CPU. Therefore, our method enables accurate and real-time tracking of multiple pedestrians. The code is publicly available at https://github.com/jinzhengguang/ELPTNet.