A solution to the simultaneous localization and map building (SLAM) problem
A solution to the simultaneous localization and map building (SLAM) problem
复制标题
DOI：
10.1109/70.938381
复制
发表时间：
2001-06-01
期刊：
影响因子：
--
通讯作者：
Csorba, M
中科院分区：
文献类型：
--
作者：
Dissanayake, MWMG;Newman, P;Csorba, M
同步定位与地图构建(SLAM)问题是问自主车辆是否有可能从未知环境中的未知位置出发，然后增量地构建该环境的地图，同时使用该地图来计算车辆的绝对位置。本文从文献[1]-[3]中提出的这一问题的估计理论基础出发，证明了SLAM问题的解确实是可能的。首先阐明了SLAM问题的基本结构。在此基础上，证明了估计映射单调收敛到相对映射的零不确定性。结果表明，地图和车辆位置的绝对精度达到了仅由初始车辆不确定性定义的下界。总而言之，这些结果表明，自动驾驶车辆有可能在未知环境中从未知位置出发，仅使用相对观测，增量地构建完美的世界地图，同时计算车辆位置的有界估计。本文还描述了SLAM算法在使用毫米波(MMW)雷达提供相对地图观测的室外环境中运行的车辆上的实质性实现。此实现用于演示如何在实际环境中处理地图管理和数据关联等关键问题。将得到的结果与测量得到的地图地标的绝对位置进行了交叉比较。最后，本文讨论了SLAM问题的解决所带来的一些关键问题，包括次优地图构建算法和地图管理。
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.