A Tutorial on Graph-Based SLAM
A Tutorial on Graph-Based SLAM
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DOI：
10.1109/mits.2010.939925
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发表时间：
2010-12-01
影响因子：
3.6
通讯作者：
Burgard, Wolfram
中科院分区：
文献类型：
--
作者：
Grisetti, Giorgio;Kuemmerle, Rainer;Burgard, Wolfram
能够建立一个地图的环境，并同时定位在这个地图是移动的机器人在未知的环境中导航，在没有外部参考系统，如GPS的基本技能。这种所谓的同时定位和地图（SLAM）的问题已经在移动的机器人在过去的二十年中最流行的研究课题之一，并提出了有效的方法来解决这个任务。制定SLAM的一种直观方式是使用图，该图的节点对应于机器人在不同时间点的姿态，并且该图的边缘表示姿态之间的约束。后者是从对环境的观察或从机器人执行的运动动作中获得的。一旦构建了这样的图，就可以通过找到与由边建模的测量结果大部分一致的节点的空间配置来计算地图。在本文中，我们提供了一个介绍性的描述基于图形的SLAM问题。此外，我们讨论了一个国家的最先进的解决方案，是基于最小二乘误差最小化，并利用结构的SLAM优化过程中的问题。本教程的目标是使读者能够从头开始实现所提出的方法。
Being able to build a map of the environment and to simultaneously localize within this map is an essential skill for mobile robots navigating in unknown environments in absence of external referencing systems such as GPS. This so-called simultaneous localization and mapping (SLAM) problem has been one of the most popular research topics in mobile robotics for the last two decades and efficient approaches for solving this task have been proposed. One intuitive way of formulating SLAM is to use a graph whose nodes correspond to the poses of the robot at different points in time and whose edges represent constraints between the poses. The latter are obtained from observations of the environment or from movement actions carried out by the robot. Once such a graph is constructed, the map can be computed by finding the spatial configuration of the nodes that is mostly consistent with the measurements modeled by the edges. In this paper, we provide an introductory description to the graph-based SLAM ;problem. Furthermore, we discuss a state-of-the- art solution that is based on least-squares error minimization and exploits the structure of the SLAM problems during optimization. The goal of this tutorial is to enable the reader to implement the proposed methods from scratch.