Sensor Planning for Mobile Robot Localization---A Hierarchical Approach Using a Bayesian Network and a Particle Filter
Sensor Planning for Mobile Robot Localization---A Hierarchical Approach Using a Bayesian Network and a Particle Filter
复制标题
DOI：
10.1109/tro.2007.912091
复制
发表时间：
2008-04
影响因子：
7.8
通讯作者：
Hongjun Zhou;S. Sakane
中科院分区：
文献类型：
--
作者：
Hongjun Zhou;S. Sakane
本文提出了一种分层求解移动机器人全局定位传感器规划问题的方法。我们的系统由两个子系统组成：较低层和较高层。下层使用粒子滤波来估计定位的后验概率。当粒子收敛成簇时，高层开始粒子聚类和传感器规划，以生成定位所需的最优感知动作序列。高层使用贝叶斯网络进行概率推理。传感器规划既考虑了定位信念，又考虑了感知成本。我们进行了仿真和实际的机器人实验来验证我们所提出的方法。
In this paper, we propose a hierarchical approach to solving sensor planning for the global localization of a mobile robot. Our system consists of two subsystems: a lower layer and a higher layer. The lower layer uses a particle filter to evaluate the posterior probability of the localization. When the particles converge into clusters, the higher layer starts particle clustering and sensor planning to generate an optimal sensing action sequence for the localization. The higher layer uses a Bayesian network for probabilistic inference. The sensor planning takes into account both localization belief and sensing cost. We conducted simulations and actual robot experiments to validate our proposed approach.