Uncertainty Reduction via Heuristic Search Planning on Hybrid Metric/Topological Map
Uncertainty Reduction via Heuristic Search Planning on Hybrid Metric/Topological Map
复制标题
通过混合度量/拓扑图的启发式搜索规划减少不确定性
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
G. Dudek
中科院分区：
文献类型：
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作者：
Qiwen Zhang;Ioannis M. Rekleitis;G. Dudek
本文对我们以前在混合度量/拓扑图上的工作进行了扩展，以便能够在考虑地图不确定性和距离的情况下通过该地图进行不确定性减少规划。在我们的启发式搜索规划算法中，为了最大限度地减少地图不确定性，提出了一种增强的边结构，使其能够通过扩展卡尔曼滤波来模拟双向边传播。这项工作扩展了[1]中提出的启发式搜索框架，使其适用于混合度量/拓扑图，而不是更受限的摄像机传感器网络。通过在真实机器人系统上的仿真和部署，实验结果表明了该算法的有效性，并验证了该方法的有效性。
This paper presents an extension of our previous work on hybrid metric/topological maps to enable uncertainty reduction planning through the map, taking into account both map uncertainty and distance. An enhancement of the edge structure which enables the simulation of bidirectional edge propagation through an extended Kalman filter is proposed in our heuristic search planning algorithm to plan for maximal map uncertainty reduction. This work expands on the heuristic search framework proposed in [1] to apply in hybrid metric/topological maps instead of more constrained camera sensor networks. Experimental results from realistic simulations and deployment on a real robotic system are presented to show the efficacy of the proposed algorithm and validate our approach for uncertainty reduction.