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Proceedings Paper

Building a million beacon map
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Paper Abstract

Many future missions for mobile robots demand multi-robot systems which are capable of operating in large environments for long periods of time. A critical capability is that each robot must be able to localize itself. However, GPS cannot be used in many environments (such as within city streets, under water, indoors, beneath foliage or extra-terrestrial robotic missions) where mobile robots are likely to become commonplace. A widely researched alternative is Simultaneous Localization and Map Building (SLAM): the vehicle constructs a map and, concurrently, estimates its own position. In this paper we consider the problem of building and maintaining an extremely large map (of one million beacons). We describe a fully distributed, highly scaleable SLAM algorithm which is based on distributed data fusion systems. A central map is maintained in global coordinates using the Split Covariance Intersection (SCI) algorithm. Relative and local maps are run independently of the central map and their estimates are periodically fused with the central map.

Paper Details

Date Published: 4 October 2001
PDF: 12 pages
Proc. SPIE 4571, Sensor Fusion and Decentralized Control in Robotic Systems IV, (4 October 2001); doi: 10.1117/12.444158
Show Author Affiliations
Simon J. Julier, IDAK Industries (United States)
Jeffrey K. Uhlmann, Univ. of Missouri/Columbia (United States)

Published in SPIE Proceedings Vol. 4571:
Sensor Fusion and Decentralized Control in Robotic Systems IV
Gerard T. McKee; Paul S. Schenker, Editor(s)

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