Place recognition from disparate views

Visual place recognition methods which use image matching techniques have shown success in recent years, however their reliance on local features restricts their use to images which are visually similar and which overlap in viewpoint. We suggest that a semantic approach to the problem would provide a more meaningful relationship between views of a place and so allow recognition when views are disparate and database coverage is sparse. As initial work towards this goal we present a system which uses detected objects as the basic feature and demonstrate promising ability to recognise places from arbitrary viewpoints. We build a 2D place model of object positions and extract features which characterise a pair of models. We then use distributions learned from training examples to compute the probability that the pair depict the same place and also an estimate of the relative pose of the cameras. Results on a dataset of 40 urban locations show good recognition performance and pose estimation, even for highly disparate views.