IMPACT ASSESSMENT OF IMAGE FEATURE EXTRACTORS ON THE PERFORMANCE OF SLAM SYSTEMS

Authors

  • Taihú Pire University of Buenos Aires, Intendente Güiraldes 2160, Ciudad Autónoma de Buenos Aires, Argentina
  • Thomas Fischer University of Buenos Aires, Intendente Güiraldes 2160, Ciudad Autónoma de Buenos Aires, Argentina
  • Jan Faigl Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27, Prague, Czech Republic

DOI:

https://doi.org/10.14311/APP.2015.1.0045

Abstract

This work evaluates an impact of image feature extractors on the performance of a visual SLAM method in terms of pose accuracy and computational requirements. In particular, the S-PTAM (Stereo Parallel Tracking and Mapping) method is considered as the visual SLAM framework for which both the feature detector and feature descriptor are parametrized. The evaluation was performed with a standard dataset with ground-truth information and six feature detectors and four descriptors. The presented results indicate that the combination of the GFTT detector and the BRIEF descriptor provides the best trade-off between the localization precision and computational requirements among the evaluated combinations of the detectors and descriptors.

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Published

2015-12-31

Issue

Vol. 2 No. 2 (2015): PAIR'14 / PAIR'15 STUDENT CONFERENCES ON PLANNING IN ARTIFICIAL INTELLIGENCE AND ROBOTICS

Section

Articles

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