Localization for Multirobot Formations in Indoor Environment
Localization for Multirobot Formations in Indoor Environment
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DOI：
10.1109/tmech.2009.2030584
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发表时间：
2010-08
期刊：
影响因子：
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通讯作者：
Haoyao Chen;Dong Sun;Jie Yang;Jian Chen
中科院分区：
文献类型：
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作者：
Haoyao Chen;Dong Sun;Jie Yang;Jian Chen
定位是多机器人编队中的一个关键问题，但目前还没有得到充分的研究。在本文中，我们提出了一个天花板视觉为基础的同时定位和地图（SLAM）的方法来解决全球定位问题的多机器人编队。首先，开发了一种有效的数据关联方法，以快速准确地实现乐观特征匹配假设。然后，利用基于匹配的方法计算机器人之间的相对位姿，进行局部定位。为了实现全球本地化的目标，提出了三种策略。第一种策略是仅全局定位一个机器人（即，leader），然后基于机器人之间的相对姿态来定位其他机器人。第二种策略是每个机器人通过单独实现SLAM来全局定位自己。第三种策略是利用可以安装在机器人之一上的公共SLAM服务器，以基于共享的全局地图同时全局定位所有机器人。最后在一组移动的机器人上进行了实验，以证明所提出的方法的有效性。
Localization is a key issue in multirobot formations, but it has not yet been sufficiently studied. In this paper, we propose a ceiling vision-based simultaneous localization and mapping (SLAM) methodology for solving the global localization problems in multirobot formations. First, an efficient data-association method is developed to achieve an optimistic feature match hypothesis quickly and accurately. Then, the relative poses among the robots are calculated utilizing a match-based approach, for local localization. To achieve the goal of global localization, three strategies are proposed. The first strategy is to globally localize one robot only (i.e., leader) and then localize the others based on relative poses among the robots. The second strategy is that each robot globally localizes itself by implementing SLAM individually. The third strategy is to utilize a common SLAM server, which may be installed on one of the robots, to globally localize all the robots simultaneously, based on a shared global map. Experiments are finally performed on a group of mobile robots to demonstrate the effectiveness of the proposed approaches.