Bags of Binary Words for Fast Place Recognition in Image Sequences

We propose a novel method for visual place recognition using bag of words obtained from accelerated segment test (FAST)+BRIEF features. For the first time, we build a vocabulary tree that discretizes a binary descriptor space and use the tree to speed up correspondences for geometrical verification....

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Vydáno v:	IEEE transactions on robotics Ročník 28; číslo 5; s. 1188 - 1197
Hlavní autoři:	Galvez-López, D., Tardos, J. D.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.10.2012 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Acceleration Applied sciences Artificial intelligence Bag of binary words Cameras Computer science; control theory; systems computer vision Control theory. Systems Data processing. List processing. Character string processing Exact sciences and technology Feature extraction Geometry Indexes Memory organisation. Data processing Pattern recognition. Digital image processing. Computational geometry place recognition Robotics Robots simultaneous localization and mapping (SLAM) Software Vectors Vocabulary Cartography Computer vision Vocabulary Image recognition Tree(graph) Moving robot Competitiveness Information retrieval Pattern recognition simultaneous localization and mapping (SLAM) Binary code Robotics Bag of words Bag of binary words Binary descriptor place recognition Selection criterion Feature extraction Localization Pattern extraction Execution time
ISSN:	1552-3098, 1941-0468
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Popis
Shrnutí:	We propose a novel method for visual place recognition using bag of words obtained from accelerated segment test (FAST)+BRIEF features. For the first time, we build a vocabulary tree that discretizes a binary descriptor space and use the tree to speed up correspondences for geometrical verification. We present competitive results with no false positives in very different datasets, using exactly the same vocabulary and settings. The whole technique, including feature extraction, requires 22 ms/frame in a sequence with 26 300 images that is one order of magnitude faster than previous approaches.
Bibliografie:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14
ISSN:	1552-3098 1941-0468
DOI:	10.1109/TRO.2012.2197158