A Global Localization and Self-Docking Method for Mobile Robot Based on Feature Map

Abstract

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.