Recognizing places using spectrally clustered local matches

Place recognition is a fundamental perceptual problem at the heart of many basic robot operations, most notably mapping. Failures can result from ambiguous sensor readings and environments with similar appearances. In this paper, we describe a robust place recognition algorithm that fuses a number of uncertain local matches into a high-confidence global match. We describe the theoretical basis of the approach and present extensive experimental results from a variety of sensor modalities and environments.