Place Recognition and Online Learning in Dynamic Scenes with Spatio-Temporal Landmarks

This paper presents a new framework for visual place recognition that incrementally learns models of each place and offers adaptability to dynamic elements in a scene. Traditional bag-of-features image-retrieval approaches to place recognition treat images in a holistic manner and are typically not capable of dealing with sub-scene dynamics, such as structural changes to a building facade or the rearrangement of furniture in a room. However, by treating local features as observations of real-world landmarks in a scene that are consistently observed, such dynamics can be accurately modelled at a local level, and the spatio-temporal properties of each landmark can be independently updated online. We propose a framework for place recognition that models each scene by sequentially learning landmarks from a set of images, and in the long term adapts the model to dynamic behaviour. Results on both indoor and outdoor datasets show an improvement in recognition performance and efficiency when compared to the traditional bag-of-features image retrieval approach.