Scene association for mobile robot navigation

Accurate, efficient and robust location recognition is a fundamental task for any mobile robot. This paper presents a new approach using visual features to efficiently represent a series of locations along a path in an indoor environment. In the training stage, local features which are detected across multiple images from a single tour are combined to represent a real-world landmark, modelled by the expected variance of its descriptor. Those landmarks which represent the scene in the most efficient and discriminative manner are then retained, and this selection is optimized with respect to the scale of the environment. In the recognition stage, features detected in an image are matched to the landmarks in memory, based upon a novel similarity measure drawing from feature co-occurrence statistics.