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A Comparison of Feature Detectors and Descriptors in RGB-D SLAM Methods

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Image Analysis and Recognition (ICIAR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9164))

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Abstract

In RGB-D based SLAM methods, robot motion is generally computed by detecting and matching feature points in image frames obtained from an RGB-D sensor. Thus, feature detectors and descriptors used in a SLAM method significantly affect the performance. In this work, impacts of feature detectors and descriptors on the performance of an RGB-D based SLAM method are studied. SIFT, SURF, BRISK, ORB, FAST, GFTT, STAR feature detectors and SIFT, SURF, BRISK, ORB, BRIEF, FREAK feature descriptors are evaluated in terms of accuracy and speed.

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Authors and Affiliations

  1. Department of Computer Engineering, Hacettepe University, Ankara, Turkey

    Oguzhan Guclu & Ahmet Burak Can

Authors
  1. Oguzhan Guclu
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  2. Ahmet Burak Can
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Correspondence to Oguzhan Guclu .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, Ontario, Canada

    Mohamed Kamel

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

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Guclu, O., Can, A.B. (2015). A Comparison of Feature Detectors and Descriptors in RGB-D SLAM Methods. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_32

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  • DOI: https://doi.org/10.1007/978-3-319-20801-5_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20800-8

  • Online ISBN: 978-3-319-20801-5

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