A model to image straight line matching method for vision-based indoor mobile robot self-location

Abstract

An efficient and simple method for matching image features to a model is presented It is designed to indoor mobile robot self-location. It is a two stage method based on the interpretation tree search approach using straight line correspondences. In the first stage a set of matching hypothesis is generated. Exploiting the specificity of the mobile robotics context, the global interpretation tree is divided into two sub-trees and then two geometric constraints are defined directly on 2D-3D correspondences in order to improve the pruning and search efficiency. In the second stage, the pose is calculated for each matching hypothesis and the best one is selected according to a defined error function. Test results illustrate the performances of the approach.