Crowd-Driven Mapping, Localization and Planning
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.
- Publication:
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arXiv e-prints
- Pub Date:
- December 2020
- DOI:
- 10.48550/arXiv.2012.10099
- arXiv:
- arXiv:2012.10099
- Bibcode:
- 2020arXiv201210099F
- Keywords:
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- Computer Science - Robotics
- E-Print:
- Accepted to ISER 2020