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Application of locality sensitive hashing to feature matching and loop closure detection Open Access


Other title
locality sensitive hashing
nearest neighbor search
parameter selection
visual slam
loop closure detection
Type of item
Degree grantor
University of Alberta
Author or creator
Shahbazi, Hossein
Supervisor and department
Zhang, Hong (Computing Science)
Examining committee member and department
Peng Zhang (Mathematical and Statistical Sciences)
Zhang, Hong (Computing Science)
Joerg Sander (Computing Science)
Department of Computing Science

Date accepted
Graduation date
Master of Science
Degree level
My thesis focuses on automatic parameter selection for euclidean distance version of Locality Sensitive Hashing (LSH) and solving visual loop closure detection by using LSH. LSH is a class of functions for probabilistic nearest neighbor search. Although some work has been done for parameter selection of LSH, having three parameters and lack of guarantees on the running time, restricts the usage of LSH. We propose a method for finding optimal LSH parameters when data distribution meets certain properties. Loop closure detection is the problem of deciding whether a robot has visited its current location before. This problem arises in both metric and visual SLAM (Simultaneous Localization and Mapping) applications and it is crucial for creating consistent maps. In our approach, we use hashing to efficiently find similar visual features. This enables us to detect loop closures in real-time without the need to pre-process the data as is the case with the Bag-of-Words (BOW) approach. We evaluate our parameter selection and loop closure detection methods by running experiments on real world and synthetic data. To show the effectiveness of our loop closure detection approach, we compare the running time and precision-recalls for our method and the BOW approach coupled with direct feature matching. Our approach has higher recall for the same precision in both sets of our experiments. The running time of our LSH system is comparable to the time that is required for extracting SIFT (Scale Invariant Feature Transform) features and is suitable for real-time applications.
License granted by Hossein Shahbazi ( on 2011-10-25T18:26:35Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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