用可见边缘定义行人组，提升复杂人群中的导航效率与安全性
LiDAR-based Crowd Navigation with Visible Edge Group Representation
- 基于可见边缘构建行人组表示，避免依赖易受遮挡影响的个体追踪
- 在高密度人群场景中，新方法导航安全性和社交性接近传统方法，但速度更快
- 实机部署验证了该方法在真实环境中的可行性和实用价值
机器人在拥挤的人群环境中导航是一项长期挑战。现有方法通常仅适用于低密度场景，或依赖外部检测模块追踪个体，易受遮挡影响。本文发现，在高密度环境中，群体预测精度对导航性能影响较小。为此，提出基于可见边缘的群体表示方法。仿真实验表明，集成简化群体表示的导航框架在密集人群下仍能保持良好的安全性和社交性，同时计算速度显著提升。最后，将该框架部署于真实机器人上，验证了群体表示在实际应用中的有效性。
原文摘要 · Abstract (English)
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.
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