BRISK-based visual feature extraction for resource constrained robots

Daniel Jaymin Mankowitz, Subramanian Ramamoorthy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We address the problem of devising vision-based feature extraction for the purpose of localisation on resource constrained robots that nonetheless require reasonably agile visual processing. We present modifications to a state-of-the-art Feature Extraction Algorithm (FEA) called Binary Robust Invariant Scalable Keypoints (BRISK) [8]. A key aspect of our contribution is the combined use of BRISK0 and U-BRISK as the FEA detector-descriptor pair for the purpose of localisation. We present a novel scoring function to find optimal parameters for this FEA. Also, we present two novel geometric matching constraints that serve to remove invalid interest point matches, which is key to keeping computations tractable. This work is evaluated using images captured on the Nao humanoid robot. In experiments, we show that the proposed procedure outperforms a previously implemented state-of-the-art vision-based FEA called 1D SURF (developed by the rUNSWift RoboCup SPL team), on the basis of accuracy and generalisation performance. Our experiments include data from indoor and outdoor environments, including a comparison to datasets such as based on Google Streetview.
Original languageEnglish
Title of host publicationRoboCup 2013: Robot World Cup XVII
PublisherSpringer Berlin Heidelberg
Pages195-206
Number of pages12
ISBN (Electronic)978-3-662-44468-9
ISBN (Print)978-3-662-44467-2
DOIs
Publication statusPublished - 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume8371
ISSN (Print)0302-9743

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