A reliable feature matching method in omnidirectional views for autonomous map generation of a mobile robot

Abstract

Deals with a matching problem of finding correspondences of features in two omnidirectional images. The proposed method combines the advantages of correlation-based matching and dynamic programming to yield reliable matching results. Our method works well even when some features in one image don't have corresponding features in the other. The search space of the dynamic programming can be reduced by the geometric constraints that the omnidirectional vision sensor provides. We also present a recursive scheme for position estimation of the matched features, so that on-line map generation is possible. Experimental results show that zero failure rate of matching in an indoor environment can be obtained and that a feature-drawing map can be successfully constructed by the proposed feature matching algorithm and map building method.