Consistent observation grouping for generating metric-topological maps that improves robot localization
Consistent observation grouping for generating metric-topological maps that improves robot localization
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DOI：
10.1109/robot.2006.1641810
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发表时间：
2006-05
期刊：
影响因子：
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通讯作者：
J. Blanco;Javier González;Juan-Antonio Fernández-Madrigal
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文献类型：
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作者：
J. Blanco;Javier González;Juan-Antonio Fernández-Madrigal
最近，混合地图，结合联合收割机度量和拓扑世界信息已被提出作为一个强大的表示移动的机器人环境。除其他外，这些地图对于有效管理大规模环境和准确定位具有特殊意义。为了实现这一点，局部几何地图被存储在基于图形的全局地图的节点中。在本文中，我们提出了一种新的方法，自动获得这些本地地图的观察。该方法将在每个观测中感测到的空间视为具有表示观测之间的空间重叠的弧的图的节点。该图的递归划分（切割）产生强连接节点组，从中导出用于精确定位的一致局部映射。建议的分区技术是有充分根据的谱图理论，它是制定任何类型的传感器观测。我们描述了一个实现分组的二维激光扫描，并显示实验结果与真实的数据，证明了该方法的性能
Recently, hybrid maps that combine metric and topological world information have been proposed as a powerful representation of mobile robot environments. Among others, these maps are of special interest for efficiently managing large-scale environments, and for accurate localization. For achieving that, local geometric maps are stored in the nodes of a graph-based global map. In this paper we present a novel approach for automatically obtaining those local maps from observations. The method considers the space sensed in each observation as a node of a graph with arcs representing the space overlap between observations. The recursive partition (cut) of this graph produces groups of strongly connected nodes from which consistent local maps for accurate localization are derived. The proposed partition technique is well-grounded in the spectral graph theory of, and it is formulated for any type of sensor observation. We depict an implementation for grouping 2D laser scans, and show experimental results with real data that demonstrate the performance of the method