Skip to main content
SpringerLink
Log in

A Single Frame Depth Visual Gyroscope and its Integration for Robot Navigation and Mapping in Structured Indoor Environments

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

An accurate navigation system is an essential and important part for the mobile robot. The recent appearance of low cost RGBD cameras has made 3D point clouds together with RGB information easy accessible, and they have been widely applied in many applications. Relative poses of a mobile robot can be estimated from consecutive visual information. However, such incremental registration methods still suffer from accumulated errors which makes the estimated trajectory as weird as by only using wheel mounted encoders. In contrast, we introduce a novel and inexpensive sensor fusion based approach to solve the robot localization problem. The key idea is to use depth visual gyroscope as a complementary source for robot heading estimation. Aided with constraints, the unscented Kalman filter is used for robot pose estimation. A field experiment has been carried out in order to verify the introduced method. Accordingly, the 3D map of the environment is also presented based on the estimated robot trajectory.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Borenstein, J., Arbor, A.: Gyrodometry: a new method for combining data from gyros and odometry in mobile robots, IEEE international Conference on Robotics and Automation (1996)

  2. Chung, H., Ojeda, L., Borenstein, J.: Accurate mobile robot dead-reckoning with a precision-calibrated fiber-optic gyroscope. IEEE Trans. Robot. Autom. 17(1), 80–84 (2001)

    Article  Google Scholar 

  3. Quellette, R, Hirasawa, K.: A comparison of SLAM implementation for indoor mobile robots, IEEE/RSJ International Conference on Intelligen Robots and Systems (IROS) (2007)

  4. Bsel, P., McKay, N.: A Method for registration of 3-D shapes. IEEE Trans. Pattern Analysis and Machine Intelligence 14, 239–256 (1992)

    Article  Google Scholar 

  5. Nchter, A., Surmann, H., Lingemann, K., Hertzberg, J., Thrun, S.: 6D SLAM with an Application in autonomous mine mapping. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1998–2003, New Orleans (2004)

  6. kerl, C., Sturm, J., Cremers, D.: Robust Odometry Estimation for RGB-D Cameras. In: Proceedings Of the IEEE Int. Conf. on Robotics and Automation (ICRA) (2013)

  7. Newcombe, R.A., Davison, A.J., Izadi, S., Kohli, O., Hilliges, O., Shotton, J., Molyneaux, D., Hodges, S., Kim, D., Fitzgibbon, A.: KinectFusion: Real-time dense surface mapping and tracking, 2011 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (2011)

  8. Taguchi, Y., Jian, Y.-D., Ramalingam, S., Feng, C.: SLAM using both points and planes for hand-held 3D sensors, Mixed and Augmented Reality (ISMAR), 2012 IEEE International Symposium on (2012)

  9. Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis and Machine Vision, 3rd edn, Stamford, Connecticut: Cengage Learning (2008)

  10. Klein, G., Drummond, T.: A Single-frame visual gyroscope. In: Proceedings of British Machine Vision Conference (BMVC) (2005)

  11. Coughlan, J.M., Yuille, A.L.: Manhattan World: Compass Direction from a Single Image by Bayesian Inference, In Internation Conference on Computer Vision (1999)

  12. Caprile, B., Torre, V.: Using Vanishing Points for camera Calibration. The International Journal of Computer Vision 4(2), 127–140 (1990)

    Article  Google Scholar 

  13. Ruotsalainen, L., Bancroft, J., Lachapelle, G., Kuusniemi, H., Chen, R.: Effect of camera characteristics on the accuracy of a visual gyroscope for indoor pedestrian navigation, Ubiquitous Position, Indoor Navigation, and Location Based Service (UPINLBS), 2012, Helsinki, Finland

  14. Ruotsalainen, L., Kuusniemi, H., Chen, R.: Visual-aided two dimensional pedestrian indoor navigation with a smartphone. J. Global Positioning Syst. 10(1) (2011)

  15. Hong, D., Lee, H., Cho, H., Park, Y., Kim, J.H.: Visual Gyroscope: Integration of Visual Information with Gyroscope for attitude Measurement of Mobile Platform, International Conference on Control, Automation and Systems, 2008, Seoul, Korea

  16. Shrivakshan, G., Chandrasekar, C.: A comparision of various edge detection techniques used in image processing. IJCSI International Journal of Computer Science Issues 9(1), 269–276 (2012)

    Google Scholar 

  17. Hough, P.V.C.: Method and means for recognizing complex patterns, U.S. Patent 3069654 (1962)

  18. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images, Sixth International Conference on Computer Vision (1998)

  19. Torr, P.H.S., Zisserman, A.: MLESAC:A new robust estimator with application to estimating image geometry. Comp. Vision Image Underst. 78, 138–156 (2000)

    Article  Google Scholar 

  20. Taguchi, Y., Jian, Y.-D., Ramalingam, S., Feng, C.: Point-plane SLAM for hand-held 3D sensors, IEEE International Conference on Robotics and Automation (ICRA), 2013, Karlsruhe, Germany

  21. Khoshelham, K., Elberink, S.O.: Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications. Sensors 12(2), 1437–1454 (2012)

    Article  Google Scholar 

  22. Nasir, A.K., Hille, C., Roth, H., Extraction, Plane: Plane Extraction and Map Building Using a Kinect Equipped Mobile Robot, Workshop on Robot Motion Planning: Online, Reactive and in Real-time, IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012, Vilamoura, Algarve, Portugal (2012)

  23. Lindner, M., Schiller, I., Kolb, A., Koch, R.: Time-of-Flight Sensor Calibration for Accurate Range Sensing. J. Comput. Vis. Image Underst. (CVIU) 114(12), 1318–1328 (2010)

    Article  Google Scholar 

  24. Simon, D.: Optime state estimation: Kalman, H, and Nonlinear Approaches. Wiley, Hoboken (2006)

    Book  Google Scholar 

  25. Simon, D.: Kalman filtering with state constraints: a survey of linear and nonlinear algorithms, IET Control Theory and Applications (2009)

  26. Teixeira, B.O.S., Chandrasekar, J., Toores, L. A.B., Dennis, L.A.A.: S.Bernstein, State estimation for linear and non-linear equality-constrained systems, International Journal of Control (2009)

Download references

Author information

Authors and Affiliations

  1. Hoelderlinstr. 3, 57068, Siegen, Germany

    Cheng Chen & Hubert Roth

  2. Center for Sensor Systems (ZESS), University of Siegen, Paul-Bonatz-Straße 9-11, 57076, Siegen, Germany

    Wennan Chai

Authors
  1. Cheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wennan Chai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hubert Roth
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cheng Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, C., Chai, W. & Roth, H. A Single Frame Depth Visual Gyroscope and its Integration for Robot Navigation and Mapping in Structured Indoor Environments. J Intell Robot Syst 80, 365–374 (2015). https://doi.org/10.1007/s10846-014-0167-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-014-0167-x

Keywords

Search

Navigation