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	Real-time SLAM relocalization with online learning of binary feature indexing
	Feng, Youji1,2; Wu, Yihong2; Fan, Lixin3
	2017-11-01
摘要	A visual simultaneous localization and mapping (SLAM) system usually contains a relocalization module to recover the camera pose after tracking failure. The core of this module is to establish correspondences between map points and key points in the image, which is typically achieved by local image feature matching. Since recently emerged binary features have orders of magnitudes higher extraction speed than traditional features such as scale invariant feature transform, they can be applied to develop a real-time relocalization module once an efficient method of binary feature matching is provided. In this paper, we propose such a method by indexing binary features with hashing. Being different from the popular locality sensitive hashing, the proposed method constructs the hash keys by an online learning process instead of pure randomness. Specifically, the hash keys are trained with the aim of attaining uniform hash buckets and high collision rates of matched feature pairs, which makes the method more efficient on approximate nearest neighbor search. By distributing the online learning into the simultaneous localization and mapping process, we successfully apply the method to SLAM relocalization. Experiments show that camera poses can be recovered in real time even when there are tens of thousands of landmarks in the map.
关键词	SLAM relocalization Binary feature indexing Approximate nearest neighbor search
DOI	10.1007/s00138-017-0873-z
发表期刊	MACHINE VISION AND APPLICATIONS
ISSN	0932-8092
卷号	28期号:8页码:953-963
通讯作者	Wu, YH (reprint author), Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China.
收录类别	SCI
WOS记录号	WOS:000413677000009
语种	英语