RGB-D Odometry Using Point and Line Features
Lighting variation and uneven feature distribution are main challenges for indoor RGB-D visual odometry where color information is often combined with depth information. To meet the challenges, we fuse point and line features to form a robust odometry algorithm. Line features are abundant indoors and less sensitive to lighting change than points. We extract 3D points and lines from RGB-D data, analyze their measurement uncertainties, and compute camera motion using maximum likelihood estimation. We prove that fusing points and lines produces smaller motion estimate uncertainty than using either feature type alone.
line detection
  • Yan Lu and Dezhen Song, "Robust RGB-D Odometry Using Point and Line Features", IEEE International Conference on Computer Vision (ICCV), 2015
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