基于直接点云可视性的UGV和无人机在线三维边界探索

Jason L. Williams, Shu Jiang, M. O'Brien, Glenn Wagner, E. Hernández, Mark Cox, Alex Pitt, R. Arkin, N. Hudson
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引用次数: 20

摘要

虽然机器人长期以来一直被认为是减少人类人员在地下环境中暴露于危险的工具,但这些环境也对这些机器人的发展提出了重大挑战。这一挑战的根本是自主探索的问题。基于边界的方法是一种强大而成功的勘探方法,但当需要在线作业时,复杂的3D环境仍然是一个挑战。本文提出了一种新的方法,通过直接建模观察到的自由空间和未观察到的空间(边界)之间的边界来解决3D操作的复杂性,而不是利用密集的3D体积表示。通过避免涉及单个地图的表示,它还实现了对同步定位和匹配(SLAM)循环闭包必不可少的问题的可伸缩性。该方法使一个由7个地面和空中机器人组成的团队能够自主探索DARPA地下挑战城市线路,在复杂和通信中断的环境中共同穿越超过8公里。
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Online 3D Frontier-Based UGV and UAV Exploration Using Direct Point Cloud Visibility
While robots have long been proposed as a tool to reduce human personnel’s exposure to danger in subterranean environments, these environments also present significant challenges to the development of these robots. Fundamental to this challenge is the problem of autonomous exploration. Frontier-based methods have been a powerful and successful approach to exploration, but complex 3D environments remain a challenge when online employment is required. This paper presents a new approach that addresses the complexity of operating in 3D by directly modelling the boundary between observed free and unobserved space (the frontier), rather than utilising dense 3D volumetric representations. By avoiding a representation involving a single map, it also achieves scalability to problems where Simultaneous Localisation and Matching (SLAM) loop closures are essential. The approach enabled a team of seven ground and air robots to autonomously explore the DARPA Subterranean Challenge Urban Circuit, jointly traversing over 8 km in a complex and communication denied environment.
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