基于霍夫空间模型匹配的移动机器人全局定位方法

提出了一种基于霍夫（Hough）空间模型匹配的全局定位方法．该方法将经典Hough变换引入移动机器人全局定位，利用摄像机获取外界环境的局部地图特征，与给定环境模型（全局地图）在Hough空间进行匹配，由Hough变换可分解性及环境模型相关性分别获取机器人可能的位姿信息，并用一系列高斯值表示，借助求取的位姿方差及其概率分布以及给定环境模型信息剔除不可能位姿，从而最终实现移动机器人全局定位．该方法尤其适用于室内结构化环境. 实验结果表明该方法具有良好的性能．

Mobile Robot Global Localization Based on Model Matching in Hough Space

This paper presents a global localization method based on model matching in Hough space. The classical Hough transform is introduced to solve this problem. To implement global localization with known environment models, a local map is firstly built via the vision system. Then the matching between known map of the environment and a local map is performed in the Hough space. By exploiting the decomposability of Hough transform and the environment model correlation, a set of possible poses represented by Gaussians is computed. By considering their covariance matrices and probability distribution as well as the information in the reference map, some inaccurate poses are discarded. The technique is especially suitable for structured environments. Experimental results validate the favorable performance of this approach.