Using visual feature and geometric constraints for robot localization

Abstract

This paper presents a novel method to generate an index word for the topological map in robot localization. Previous studies extract only appearance features from an input image to match the visual words of the model images. However, the localization performance is much affected by the miss or false matches. First, we segment a robot navigation environment into the structural planes using 3D depth data. We obtain both the surface normal vectors of the structural planes and visual features in the model image, which are compared with those of an input request image in the voting approach. The experimental results show the voting performance is improved by taking into account the spatial distribution of the features. © 2015 SERSC.