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Evaluation of Recent Approaches to Visual Odometry from RGB-D Images

  • Sergey Alexandrov
  • Rainer Herpers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8371)

Abstract

Estimation of camera motion from RGB-D images has been an active research topic in recent years. Several RGB-D visual odometry systems were reported in literature and released under open-source licenses. The objective of this contribution is to evaluate the recently published approaches to motion estimation. A publicly available dataset of RGB-D sequences with precise ground truth data is applied and results are compared and discussed. Experiments on a mobile robot used in the RoboCup@Work league are discussed as well. The system showing the best performance is capable of estimating the motion with drift as small as 1\(^{{cm}\over{s}}\) under special conditions, though it has been proven to be robust against shakey motion and moderately non-static scenes.

Keywords

Ground Truth Camera Motion Iterative Close Point Rotational Component Visual Odometry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Scaramuzza, D., Fraundorfer, F.: Visual Odometry (Tutorial). IEEE Robotics & Automation Magazine 18(4), 80–92 (2011)CrossRefGoogle Scholar
  2. 2.
    Howard, A.: Real-time Stereo Visual Odometry for Autonomous Ground Vehicles. In: Proc. of IROS (2008)Google Scholar
  3. 3.
    Huang, A.S., Bachrach, A., Henry, P., Krainin, M., Maturana, D., Fox, D., Roy, N.: Visual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera. In: Int. Symposium on Robotics Research, ISRR (2011)Google Scholar
  4. 4.
    Pomerleau, F., Magnenat, S., Colas, F., Liu, M., Siegwart, R.: Tracking a Depth Camera: Parameter Exploration for Fast ICP. In: Proc. of IROS (2011)Google Scholar
  5. 5.
    Domínguez, S., Zalama, E., García-Bermejo, J.G., Worst, R., Behnke, S.: Fast 6D Odometry Based on Visual Features and Depth. In: Lee, S., Yoon, K.-J., Lee, J. (eds.) Frontiers of Intelligent Auton. Syst. SCI, vol. 466, pp. 5–16. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Steinbrücker, F., Sturm, J., Cremers, D.: Real-Time Visual Odometry from Dense RGB-D Images. In: Workshops at ICCV (2011)Google Scholar
  7. 7.
    Kerl, C., Sturm, J., Cremers, D.: Robust Odometry Estimation for RGB-D Cameras. In: Proc. of ICRA (2013)Google Scholar
  8. 8.
    Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments. In: Proc. of the Int. Symposium on Experimental Robotics (2010)Google Scholar
  9. 9.
    Du, H., Henry, P., Ren, X., Cheng, M., Goldman, D., Seitz, S., Fox, D.: Interactive 3D Modeling of Indoor Environments with a Consumer Depth Camera. In: Proc. of the Int. Conf. on Ubiquitous Computing (2011)Google Scholar
  10. 10.
    Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., Burgard, W.: An Evaluation of the RGB-D SLAM System. In: Proc. of ICRA (2012)Google Scholar
  11. 11.
    Hu, G., Huang, S., Zhao, L., Alempijevic, A., Dissanayake, G.: A Robust RGB-D SLAM Algorithm. In: Proc. of IROS (2012)Google Scholar
  12. 12.
    Sünderhauf, N., Protzel, P.: Stereo Odometry - A Review of Approaches. Electrical Engineering (2007)Google Scholar
  13. 13.
    Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A Benchmark for the Evaluation of RGB-D SLAM Systems. In: Proc. of IROS (2012)Google Scholar
  14. 14.
    Bischoff, R., Huggenberger, U., Prassler, E.: KUKA youBot - A Mobile Manipulator for Research and Education. In: Proc. of ICRA (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Sergey Alexandrov
    • 1
  • Rainer Herpers
    • 1
    • 2
    • 3
  1. 1.Department of Computer ScienceHochschule Bonn-Rhein-SiegGermany
  2. 2.Department of Computer Science and EngineeringYork UniversityCanada
  3. 3.Faculty of Computer ScienceUniversity of New BrunswickCanada

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