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

GÜÇLÜ O., CAN A. B.

12th International Conference on Image Analysis and Recognition (ICIAR), Niagara Falls, Kanada, 22 - 24 Temmuz 2015, cilt.9164, ss.297-305 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 9164
  • Doi Numarası: 10.1007/978-3-319-20801-5_32
  • Basıldığı Şehir: Niagara Falls
  • Basıldığı Ülke: Kanada
  • Sayfa Sayıları: ss.297-305
  • Hacettepe Üniversitesi Adresli: Evet

Özet

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.