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Hybrid Mobile Robot Indoor Localization Using Large-Scale Metric-Topological Map

机译:混合移动机器人室内定位使用大规模度量拓扑图

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Localization is an essential task for an autonomous mobile robot's indoor navigation. Based on the metric-topological environment modeling, a hybrid localization method, pose tracking in local metric map and global localization in topological map, are used in this paper to carry out accurate and robust localization in semi-structured indoor environment. In order to guarantee the effectiveness of global localization and also solve the kidnapped robot problem in dynamic environment with some unexpected collisions, a novel Normalized Topological Node Weights Filter (NTNWF) algorithm is presented, which makes mobile robot's localization in large-scale topological map with closing loops and symmetric configurations available and efficient in least motion steps. Experiment results implemented in both SmartROB-2 mobile robot and the simulation software designed specially for NTNWF algorithm show the hybrid localization method's validity and practicability.
机译:本地化是自主移动机器人室内导航的重要任务。基于公制拓扑环境建模,本文采用了一种混合定位方法,拓扑地图中的全局定位,在半结构化室内环境中进行准确且稳健的本地化。为了保证全球本地化的有效性,还通过一些意外碰撞解决了动态环境中的绑架机器人问题,提出了一种新的归一化拓扑节点权重滤波器(NTNWF)算法,使移动机器人在大规模拓扑地图中的本地化关闭循环和对称配置可用和有效地以最小运动步骤。在Smartrob-2移动机器人和专门用于NTNWF算法设计的仿真软件中实施的实验结果显示了混合本地化方法的有效性和实用性。

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