NDLI: SuperFAST: Model-based adaptive corner detection for scalable robotic vision

Content Provider	IEEE Xplore Digital Library
Author	Florentz, G. Aldea, E.
Copyright Year	2014
Description	Author affiliation: Parrot S.A., Paris, France (Florentz, G.) \|\| Autonomous Syst. Dept., Univ. Paris-Sud, Orsay, France (Aldea, E.)
Abstract	In this study, we propose a novel solution to regulate the amount of interest points extracted from an image without significant additional computational cost. Our method acts at the very beginning of the detection process by using a corner occurrence model in order to predict the optimal threshold for a user-defined number of detections. Compared to existing approaches which guarantee a reasonable amount of corners by using a low threshold and then pruning the result, our approach is faster and more regular in terms of computation time as it avoids scoring and sorting the detected corners. Using the FAST detector as testbed, the strategy outlined in this article is evaluated in typical environments for robotics applications, and we report improved detection reliability during important scene variations. Taking into account the underlying visual navigation algorithms, we show that by regularizing the data input our solution facilitates a stable processing load, lower inter-frame computation time, and robustness to scene variations.
Starting Page	1003
Ending Page	1010
File Size	965361
Page Count	8
File Format	PDF
e-ISBN	9781479969340
DOI	10.1109/IROS.2014.6942681
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Detectors Adaptation models Data models Extrapolation Mathematical model Robots
Content Type	Text
Resource Type	Article

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