An Experimental and Theoretical Investigation into Simultaneous Localisation and Map Building
An Experimental and Theoretical Investigation into Simultaneous Localisation and Map Building
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DOI：
10.1007/bfb0119405
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发表时间：
1999-03
期刊：
影响因子：
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通讯作者：
G. Dissanayake;P. Newman;H. Durrant-Whyte;S. Clark;M. Csorba
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文献类型：
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作者：
G. Dissanayake;P. Newman;H. Durrant-Whyte;S. Clark;M. Csorba
同步定位和地图构建（SLAM）问题询问自动驾驶车辆是否可以在未知环境中的未知位置启动，然后逐步构建该环境的地图，同时使用该地图计算绝对车辆位置。本文从文献[5，4，2]中提出的这一问题的估计理论基础出发，证明了SLAM问题的解决方案是可能的。首先阐明了SLAM问题的基本结构。证明了估计的映射单调收敛到相对映射的零不确定性。然后示出的地图和车辆位置的绝对精度达到仅由初始车辆的不确定性定义的下限。总之，这些结果表明，自动驾驶车辆可以在未知环境中的未知位置启动，并且仅使用相对观测，逐步构建完美的世界地图，同时计算车辆位置的有界估计。
The simultaneous localisation 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 [5, 4, 2], 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 simultaneously to compute a bounded estimate of vehicle location.