Multi-Robot Mapping using Manifold Representations
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This paper introduces a new method for representing two-dimensional maps, and shows how this representation may be applied to concurrent localization and mapping problems involving multiple robots. We introduce the notion of a manifold map; this representation takes maps out of the plane and onto a two-dimensional surface embedded in a higher-dimensional space. Compared with standard planar maps, the key advantage of the manifold representation is self-consistency: as we will show, manifold maps do not suffer from the `cross over' problem that planar maps commonly exhibit in environments containing loops. This self-consistency facilitates a number of important autonomous capabilities, including robust retro-traverse, lazy loop closure, active loop closure using robot rendezvous, and, ultimately, autonomous exploration.