中文摘要：

提出一种新的移动机器人全局定位与自主泊位方法.该方法分为两阶段：离线阶段,采用SIFT（Scale In-variant Feature Transform）算法并提出一种基于DD-BBF（Double Direction Best Bin First）的特征匹配方法实现视觉特征三维重建;将进化策略应用于Rao-Blackwellized粒子滤波器,并结合自适应重采样,实现了移动机器人同时定位和特征地图创建.在线阶段,采用基于HMM（Hidden Markov Model）的方法实现全局泊位位置识别;采用RANSAC算法实现全局度量定位;提出极点伺服控制方法,实现机器人精确自主泊位.在室内环境下的实验结果证实了该方法的优良性能.

英文摘要：

A global localization and self-docking method for mobile robot is presented.The method is composed of two stages：during the off-line stage,SIFT（scale invariant feature transform） algorithm is used and a DD-BBF（double direction best bin first） matching method is presented to implement the 3-D reconstruction of vision features;an ES（evolution strategy） and adaptive re-sampling scheme were applied in RBPF（Rao-Blackwellized particle filter） to implement the mobile robot SLAM（simultaneous localization and mapping）.In the on-line stage,the global docking station is recognized through HMM（Hidden Markov Model） based methotd,he global metric pose and location of the robot are estimated by a RANSAC algorithm;and then an epipole servoing method is presented to dock the robot precisely.Experiment results carried out with a real robot in an indoor environment show the superior performance of the proposed method.