论文标题
人群驱动的映射，本地化和计划
Crowd-Driven Mapping, Localization and Planning
论文作者
论文摘要
在密集的人群中导航是机器人技术中众所周知的开放问题，在映射，本地化和计划方面面临许多挑战。传统解决方案将密集的行人视为所有麻烦原因的被动/主动移动障碍：它们对静态场景地标的感知产生负面影响，并且必须积极避免为安全而避免。在本文中，我们提供了一个新的视角：当地观察到的人群可以被视为有关周围场景的感官测量，不仅编码了场景的遍历性，还编码了其社交导航的偏好。我们证明，即使单独使用众群测量，我们的方法仍然在密集的人群中取得了良好的映射，本地化和社会意识计划的效果。实验的视频可在https://sites.google.com/view/crowdmapping上找到。
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.
