• DOI:10.1109/ICCVW.2011.6130321
• Corpus ID: 2007767

Real-time visual odometry from dense RGB-D images

@article{Steinbrcker2011RealtimeVO,
  title={Real-time visual odometry from dense RGB-D images},
  author={Frank Steinbr{\"u}cker and J{\"u}rgen Sturm and Daniel Cremers},
  journal={2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)},
  year={2011},
  pages={719-722}
}

• Frank Steinbrücker, Jürgen Sturm, D. Cremers
• Published 1 November 2011
• Computer Science
• 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)

We present an energy-based approach to visual odometry from RGB-D images of a Microsoft Kinect camera. To this end we propose an energy function which aims at finding the best rigid body motion to map one RGB-D image into another one, assuming a static scene filmed by a moving camera. We then propose a linearization of the energy function which leads to a 6×6 normal equation for the twist coordinates representing the rigid body motion. To allow for larger motions, we solve this equation in a coarse-to-fine scheme. Extensive quantitative analysis on recently proposed benchmark datasets shows that the proposed solution is faster than a state-of-the-art implementation of the iterative closest point (ICP) algorithm by two orders of magnitude. While ICP is more robust to large camera motion, the proposed method gives better results in the regime of small displacements which are often the case in camera tracking applications.