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A solution to the simultaneous localization and map building (SLAM) problem (2001)
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| Venue: | IEEE Transactions on Robotics and Automation |
| Citations: | 505 - 30 self |
BibTeX
@ARTICLE{Dissanayake01asolution,
author = {M. W. M. Gamini Dissanayake and Paul Newman and Steven Clark and Hugh F. Durrant-whyte and M. Csorba},
title = {A solution to the simultaneous localization and map building (SLAM) problem},
journal = {IEEE Transactions on Robotics and Automation},
year = {2001},
volume = {17},
pages = {229--241}
}
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Abstract
Abstract—The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from the estimation-theoretic foundations of this problem developed in [1]–[3], this paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed. It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty. Together, these results show that it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and to compute simultaneously a bounded estimate of vehicle location. This paper also describes a substantial implementation of the SLAM algorithm on a vehicle operating in an outdoor environment using millimeter-wave (MMW) radar to provide relative map observations. This implementation is used to demonstrate how some key issues such as map management and data association can be handled in a practical environment. The results obtained are cross-compared with absolute locations of the map landmarks obtained by surveying. In conclusion, this paper discusses a number of key issues raised by the solution to the SLAM problem including suboptimal map-building algorithms and map management. Index Terms—Autonomous navigation, millimeter wave radar, simultaneous localization and map building. I.
Keyphrases
simultaneous localization map building slam problem unknown location unknown environment map management autonomous vehicle key issue vehicle location underlying structure estimation-theoretic foundation map converges vehicle operating absolute vehicle location absolute accuracy slam algorithm perfect map practical environment millimeter wave radar bounded estimate outdoor environment index term autonomous navigation absolute location suboptimal map-building algorithm relative map initial vehicle uncertainty relative map observation data association map landmark substantial implementation relative observation
