Multirobot Simultaneous Localization and Mapping Using Manifold Representations
Abstract
This paper describes a novel representation for two-dimensional maps, and shows how this representation may be applied to the problem of multi-robot simultaneous localization and mapping (SLAM). 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 multi-robot simultaneous localization and mapping problem, and present experimental results obtained from teams of up to four robots in environments ranging in size from 400 square meters to 900 square meters.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 2006
- Accession Number
- ADA575687
Entities
People
- Andrew J. Howard
- Gaurav S. Sukhatme
- Maja Matarić
Organizations
- University of Southern California