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Real-time SLAM Relocalization with On-line Learning of Binary Feature Indexing
Wu YH(吴毅红)
Source PublicationMachine Vision and Applications
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.
KeywordSlam Camera Relocalization
Document Type期刊论文
Recommended Citation
GB/T 7714
Wu YH. Real-time SLAM Relocalization with On-line Learning of Binary Feature Indexing[J]. Machine Vision and Applications,2017(28):953-963.
APA Wu YH.(2017).Real-time SLAM Relocalization with On-line Learning of Binary Feature Indexing.Machine Vision and Applications(28),953-963.
MLA Wu YH."Real-time SLAM Relocalization with On-line Learning of Binary Feature Indexing".Machine Vision and Applications .28(2017):953-963.
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