Dynamic scene models for incremental, long-term, appearance-based localisation

In this paper we present a new appearance-based localisation system that is able to deal with dynamic elements in the scene. By independently modelling the properties of local features observed in a scene over long periods of time, we show that feature appearances and geometric relationships can be learned more accurately than when representing a location by a single image. We also present a new dataset consisting of a 6 km outdoor path traversed once per month for a period of 5 months, which contains several challenges including short-term and long-term dynamic behaviour, lateral deviations in the path, repetitive scene appearances and strong illumination changes. We show superior performance of the dynamic mapping system compared to state-of-the-art techniques on our dataset.