S 2 MAT: Simultaneous and Self-Reinforced Mapping and Tracking in
Dynamic Urban Scenarios
Abstract
Despite the increasing prevalence of robots in daily life, their
navigation capabilities are still limited to environments with prior
knowledge, such as a global map. To fully unlock the potential of
robots, it is crucial to enable them to navigate in large-scale unknown
and changing unstructured scenarios. This requires the robot to
construct an accurate static map in real-time as it explores, while
filtering out moving objects to ensure mapping accuracy and, if
possible, achieving high-quality pedestrian tracking and collision
avoidance. While existing methods can achieve individual goals of
spatial mapping or dynamic object detection and tracking, there has been
limited research on effectively integrating these two tasks, which are
actually coupled and reciprocal. In this work, we propose a solution
called S 2MAT (Simultaneous and Self-Reinforced
Mapping and Tracking) that integrates a front-end dynamic object
detection and tracking module with a back-end static mapping module. S
2MAT leverages the close and reciprocal interplay
between these two modules to efficiently and effectively solve the open
problem of simultaneous tracking and mapping in highly dynamic
scenarios. The proposed method is primarily designed for use with 3D
LiDAR and offers a solution for real-time navigation in large-scale,
unknown dynamic scenarios with a low computational cost, making it
feasible for deployment on onboard computers equipped with only a single
CPU. We conducted long-range experiments in real-world urban scenarios
spanning over 7km, which included challenging obstacles like pedestrians
and other traffic agents. The successful navigation provides a
comprehensive test of S 2MAT’s robustness,
scalability, efficiency, quality, and its ability to benefit autonomous
robots in wild scenarios without pre-built maps.