Large scale graph-based SLAM using aerial images as prior information
Large scale graph-based SLAM using aerial images as prior information
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DOI：
10.1007/s10514-010-9204-1
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发表时间：
2011-01-01
影响因子：
3.5
通讯作者：
Burgard, Wolfram
中科院分区：
文献类型：
--
作者：
Kuemmerle, Rainer;Steder, Bastian;Burgard, Wolfram
移动机器人学习地图的问题在过去已经得到了广泛的研究，通常被称为同时定位和地图绘制(SLAM)问题。然而，SLAM问题的大多数现有解决方案都是从头开始学习地图，无法纳入先前的信息。在本文中，我们提出了一种新的SLAM方法，该方法通过利用公开可访问的航空照片作为先验信息来实现全局一致性。它将立体和三维距离数据与航空图像之间的对应关系作为约束插入到基于图形的SLAM问题的公式中。我们基于在室内和室外混合环境中获取的大量真实数据集来评估我们的算法，方法是将全局精度与最先进的SLAM方法和GPS进行比较。实验结果表明，该方法得到的地图具有较高的全局一致性。
The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, most existing solutions to the SLAM problem learn the maps from scratch and have no means for incorporating prior information. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. It inserts correspondences found between stereo and three-dimensional range data and the aerial images as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired even in mixed in- and outdoor environments by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.