Crowd-Driven Mapping, Localization and Planning

TL;DR

This paper introduces a novel approach to navigation in dense crowds by using crowd flow as a sensory measurement, enabling mapping, localization, and planning without relying on static obstacle sensing.

Contribution

It presents a new perspective that crowd flow can serve as a sensory input for navigation tasks, reducing dependence on traditional static obstacle sensing.

Findings

Crowd flow alone can enable effective mapping and localization.

The method achieves good results in dense crowd scenarios.

Social-aware planning is possible using crowd flow data.

Abstract

Navigation in dense crowds is a well-known open problem in robotics with many challenges in mapping, localization, and planning. Traditional solutions consider dense pedestrians as passive/active moving obstacles that are the cause of all troubles: they negatively affect the sensing of static scene landmarks and must be actively avoided for safety. In this paper, we provide a new perspective: the crowd flow locally observed can be treated as a sensory measurement about the surrounding scenario, encoding not only the scene's traversability but also its social navigation preference. We demonstrate that even using the crowd-flow measurement alone without any sensing about static obstacles, our method still accomplishes good results for mapping, localization, and social-aware planning in dense crowds. Videos of the experiments are available at https://sites.google.com/view/crowdmapping.