LiDAR-based Crowd Navigation with Visible Edge Group Representation

2026-04-17

AI Summary

This work addresses the challenges of safety, efficiency, and social compliance in robot navigation within high-density crowds, where existing approaches either rely on individual detection—vulnerable to occlusions—or are limited to low-density scenarios. To overcome these limitations, the authors propose a lightweight group representation based on visible boundaries that captures crowd structure without requiring precise individual tracking. This representation is integrated with LiDAR-based perception to form an efficient navigation framework. Experimental results demonstrate that the proposed method achieves safety and social compliance comparable to state-of-the-art approaches in dense crowds, while significantly reducing computational complexity. Consequently, it offers strong real-time performance and practical potential for real-world deployment.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Natural Language Processing: Safety and Robustness
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Large-scale security measurements

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.

Problem

• crowd navigation
• group representation
• LiDAR
• occlusion
• mobile robot

Innovation

• visible edge group representation
• LiDAR-based navigation
• crowd navigation
• social robot navigation
• group modeling