LiDAR-based Crowd Navigation with Visible Edge Group Representation

TL;DR

This paper introduces a visible edge-based group representation for LiDAR-based robot navigation in dense crowds, improving safety, socialness, and computational efficiency in real-world deployments.

Contribution

It proposes a novel visible edge-based group representation that maintains navigation performance while reducing reliance on complex detection modules.

Findings

Group prediction accuracy has limited impact on navigation in dense crowds.

The proposed method achieves faster computation without sacrificing safety or socialness.

Successful real-world deployment demonstrates practical benefits of the approach.

Abstract

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.