Abstract

Place recognition currently suffers from a lack of scalability due to the need for strong geometric constraints, which as of yet are typ- ically limited to RANSAC implementations. In this paper, we present a method to successfully achieve state-of-the-art performance, in both recognition accuracy and speed, without the need for RANSAC. We pro- pose to discretise each feature pair in an image, in both appearance and 2D geometry, to create a triplet of words: one each for the appearance of the two features, and one for the pairwise geometry. This triplet is then passed through an inverted index to find examples of such pairwise configurations in the database. Finally, a global geometry constraint is enforced by considering the maximum-clique in an adjacency graph of pairwise correspondences. The discrete nature of the problem allows for tractable probabilistic scores to be assigned to each correspondence, and the least informative feature pairs can be eliminated from the database for memory and time efficiency. We demonstrate the performance of our method on several large-scale datasets, and show improvements over sev- eral baselines.