Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Randomized RANSAC with Sequential Probability Ratio Test
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Preemptive RANSAC for live structure and motion estimation
Machine Vision and Applications
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Holography map for home robot: an object-oriented approach
Intelligent Service Robotics
A novel loop closure detection method in monocular SLAM
Intelligent Service Robotics
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We outline an approach for using concentration inequalities to perform rapid approximate multi-hypothesis testing. In a scenario where multiple hypotheses are ranked according to a large set of features, our scheme improves the efficiency of selecting the best hypothesis by providing a "bail-out threshold" atwhich unpromising hypotheses can be excluded fromfurther evaluation. We show how concentration inequalities can be used to derive principled bail-out thresholds, subject to a user-specified error tolerance. The technique is similar to the sequential probability ratio test, but is applicable in more general conditions. We apply the technique to improve the speed of the fast-appearance-based mapping system for appearance-based place recognition and mapping. The speed increase provided by the new approach is data dependent, but we demonstrate speed improvements of between 25x - 50x on real data, with only a slight degradation in accuracy.