LiDAR-based Crowd Navigation with Visible Edge Group Representation
For mobile robot navigation in dense crowds, this work offers a practical group representation that reduces computational overhead without sacrificing performance, though the improvement is incremental.
The paper tackles robot navigation in crowded pedestrian environments using group-based representations. It finds that group prediction accuracy has marginal impact on navigation performance in crowds, and proposes a visible edge-based group representation that achieves comparable safety and socialness with faster computation, validated in simulation and on a real robot.
Robot navigation in crowded pedestrian environments is a well-known challenge and we explore the practical deployment of group-based representations in this setting. Pedestrian groups have been empirically shown to enable a mobile robot's navigation behavior to be safer and more social. However, existing approaches either explored groups only in limited scenarios with no high-density crowds or depended on external detection modules to track individuals, which are prone to noise and errors due to occlusions in crowds. We show that group prediction accuracy affects navigation performance only marginally in crowded environments. Based on this observation, we propose the visible edge-based group representation. We additionally demonstrate via simulation experiments that our navigation framework, integrated with the simplified group representation, performs comparatively in terms of safety and socialness in dense crowds, while achieving faster computation speed. Finally, we deploy our navigation framework on a real robot to explore the benefits of practically deploying group-based representations in the real world.