BRISK-Based Visual Feature Extraction for Resource Constrained Robots

  • Daniel Jaymin Mankowitz
  • Subramanian Ramamoorthy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8371)


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.


BRISK BRISK0 - U-BRISK feature extraction localisation resource constrained robot Nao Humanoid Robot 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Daniel Jaymin Mankowitz
    • 1
  • Subramanian Ramamoorthy
    • 1
  1. 1.School of InformaticsUniversity of EdinburghEdinburghUK

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