Journal
IEEE TRANSACTIONS ON ROBOTICS
Volume 29, Issue 3, Pages 734-745Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TRO.2013.2242375
Keywords
Appearance-based localization and mapping; bag-of-words approach; dynamic Bayes filtering; place recognition
Categories
Funding
- Natural Sciences and Engineering Research Council of Canada
- Canadian Foundation for Innovation
- Canada Research Chair program
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In appearance-based localization and mapping, loop-closure detection is the process used to determinate if the current observation comes from a previously visited location or a new one. As the size of the internal map increases, so does the time required to compare new observations with all stored locations, eventually limiting online processing. This paper presents an online loop-closure detection approach for large-scale and long-term operation. The approach is based on a memory management method, which limits the number of locations used for loop-closure detection so that the computation time remains under real-time constraints. The idea consists of keeping the most recent and frequently observed locations in a working memory (WM) that is used for loop-closure detection, and transferring the others into a long-term memory (LTM). When a match is found between the current location and one stored in WM, associated locations that are stored in LTM can be updated and remembered for additional loop-closure detections. Results demonstrate the approach's adaptability and scalability using ten standard datasets from other appearance-based loop-closure approaches, one custom dataset using real images taken over a 2-km loop of our university campus, and one custom dataset (7 h) using virtual images from the racing video game Need for Speed: Most Wanted.
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