View-based Maps
View-based Maps
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DOI：
10.1177/0278364910370376
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发表时间：
2010-07
期刊：
影响因子：
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通讯作者：
K. Konolige;James Bowman;Jindong Chen;P. Mihelich;Michael Calonder;V. Lepetit;P. Fua
中科院分区：
文献类型：
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作者：
K. Konolige;James Bowman;Jindong Chen;P. Mihelich;Michael Calonder;V. Lepetit;P. Fua
能够实时创建和使用视觉地图的机器人系统在许多应用中具有明显的优势，从自动驾驶到家庭中的移动操作。在本文中，我们描述了一个基于保留机器人移动时收集的环境立体视图的映射系统。视图之间的联系是通过其特征的一致几何匹配形成的。乱序匹配是关键问题：如何找到从当前视图到地图中其他相应视图的连接。我们的方法使用词汇树来提出候选视图，并使用强大的几何过滤器来消除误报：本质上，机器人不断地重新识别它所在的位置。我们的实验展示了该方法在视频数据上的实用性，包括在大型室内和室外环境中增量地图构建，没有定位的地图构建，以及丢失时的重新定位。
Robotic systems that can create and use visual maps in real-time have obvious advantages in many applications, from automatic driving to mobile manipulation in the home. In this paper we describe a mapping system based on retaining stereo views of the environment that are collected as the robot moves. Connections among the views are formed by consistent geometric matching of their features. Out-of-sequence matching is the key problem: how to find connections from the current view to other corresponding views in the map. Our approach uses a vocabulary tree to propose candidate views, and a strong geometric filter to eliminate false positives: essentially, the robot continually re-recognizes where it is. We present experiments showing the utility of the approach on video data, including incremental map building in large indoor and outdoor environments, map building without localization, and re-localization when lost.