Multirobot Simultaneous Localization and Mapping Using Manifold Representations - Data from exploring robots can be used to map individual robot paths separately; where robots meet, the paths may be merged to form a larger map

摘要

This paper describes a novel representation for two-dimensional maps, and shows how this representation may be applied to the problem of multirobot simultaneous localization and mapping. We are inspired by the notion of a manifold, which takes maps out of the two-dimensional plane and onto a surface embedded in a higher-dimensional space. The key advantage of the manifold representation is self-consistency: when closing loops, manifold maps do not suffer from the "cross over" problem exhibited in planar maps. This self-consistency, in turn, facilitates a number of important capabilities, including autonomous exploration, search, and retro-traverse. It also supports a very robust form of loop closure, in which pairs of robots act collectively to confirm or reject possible correspondence points. In this paper, we develop the basic formalism of the manifold representation, show how this may be applied to the multirobot simultaneous localization and mapping problem, and present experimental results obtained from teams of up to four robots in environments ranging in size from 400 to 900 m{sup}2.