Using context to solve the correspondence problem in Simultaneous Localisation and Mapping

Abstract

We present a method for solving the correspondence problem in Simultaneous Localisation and Mapping (SLAM) in a topological map. The nodes in the topological map are a representation for each local space the robot visits. The approach is feature based - a neural network algorithm is used to learn a signature from a set of features extracted from each local space representation. Newly encountered local spaces are classified by the neural network as to how well they match the signatures of the nodes in the topological network. Of equal importance as the correspondence problem is its dual, that of perceptual aliasing which occurs when parts of the environment which appear the same are in fact different. It manifests itself as false positive matches from the neural network classification. Our approach to solving this aspect of the problem is to use the context provide by nodes in the neighbourhood of the (mis)matched node. When neural network classification indicates a correspondence then subsequent local spaces the robot visits should also match nodes in the topological map where appropriate.