Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

FVO: floor vision aided odometry

Abstract

In many indoor scenarios, such as restaurants, laboratories, and supermarkets, the planar floors are covered with rectangular tiles. We realized that the abundant parallel lines and crossing points formed by tile joints can be used as natural features to assist indoor localization, and thus we propose a novel indoor localization method for mobile robots by fusing odometry and monocular vision. The method comprises three steps. First, the heading and location of the mobile robot are approximately estimated by odometry based on incremental encoders. Second, with the aid of a camera, the lens of which points vertically toward the floor, the odometric heading estimation can be corrected by detecting the relative angle between the robot’s heading and the tile joints. Third, the odometric location estimation is corrected by detecting the perpendicular distance between the image center and the tile joints. As compared with the existing indoor localization methods, the proposed method, called floor vision aided odometry, is not only relatively low in economic cost and computational complexity, but also relatively high in accuracy and robustness. The effectiveness of this method is verified by a real-world experiment based on a differential-drive wheeled mobile robot.

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

References

  1. 1

    Ye C L, Ma S G, Hui L. An omnidirectional mobile robot. Sci China Inf Sci, 2011, 54: 2631–2638

  2. 2

    Lv W J, Kang Y, Qin J H. Indoor localization for skid-steering mobile robot by fusing encoder, gyroscope, and magnetometer. IEEE Trans Syst Man Cybern Syst, 2017. doi: 10.1109/TSMC.2017.2701353

  3. 3

    Breuer T, Macedo G R G, Hartanto R, et al. Johnny: an autonomous service robot for domestic environments. J Intel Robot Syst, 2012, 66: 245–272

  4. 4

    Siegwart R, Nourbakhsh I R, Scaramuzza D. Introduction to Autonomous Mobile Robots. 2nd ed. Cambridge: MIT Press, 2011

  5. 5

    Li K, Ji H B. Inverse optimal adaptive backstepping control for spacecraft rendezvous on elliptical orbits. Int J Control, 2017, 7: 1–11

  6. 6

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

  7. 7

    Kim J H, Lee J C. Dead-reckoning scheme for wheeled mobile robots moving on curved surfaces. J Intel Robot Syst, 2015, 79: 211–220

  8. 8

    Reinstein M, Kubelka V, Zimmermann K. Terrain adaptive odometry for mobile skid-steer robots. In: Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, 2013. 4706–4711

  9. 9

    Lee H, Jung J, Choi K, et al. Fuzzy-logic-assisted interacting multiple model (FLAIMM) for mobile robot localization. Robot Auton Syst, 2012, 60: 1592–1606

  10. 10

    Borenstein J, Feng L. Gyrodometry: a new method for combining data from gyros and odometry in mobile robots. In: Proceedings of the 1996 IEEE International Conference on Robotics and Automation, Minneapolis, 1996. 423–428

  11. 11

    Myung H, Lee H K, Choi K, et al. Mobile robot localization with gyroscope and constrained Kalman filter. Int J Control Autom Syst, 2010, 8: 667–676

  12. 12

    Georgy J, Noureldin A, Korenberg M J, et al. Modeling the stochastic drift of a MEMS-based gyroscope in Gyro/Odometer/GPS integrated navigation. IEEE Trans Intel Transp Syst, 2010, 11: 856–872

  13. 13

    Garcia-Valverde T, Garcia-Sola A, Hagras H, et al. A fuzzy logic-based system for indoor localization using WiFi in ambient intelligent environments. IEEE Trans Fuzzy Syst, 2013, 21: 702–718

  14. 14

    Yang P, Wu W Y. Efficient particle filter localization algorithm in dense passive RFID tag environment. IEEE Trans Ind Electron, 2014, 61: 5641–5651

  15. 15

    Yasir M, Ho S W, Vellambi B N. Indoor positioning system using visible light and accelerometer. J Lightwave Technol, 2014, 32: 3306–3316

  16. 16

    Jung J, Lee S M, Myung H. Indoor mobile robot localization and mapping based on ambient magnetic fields and aiding radio sources. IEEE Trans Instrum Meas, 2015, 64: 1922–1934

  17. 17

    Hoermann S, Borges P V K. Vehicle localization and classification using off-board vision and 3-D models. IEEE Trans Robot, 2014, 30: 432–447

  18. 18

    How J P, Behihke B, Frank A, et al. Real-time indoor autonomous vehicle test environment. IEEE Control Syst Mag, 2008, 28: 51–64

  19. 19

    Panich S, Afzulpurkar N. Mobile robot integrated with gyroscope by using IKF. Int J Adv Robot Syst, 2011, 8: 122–136

  20. 20

    Wang W S, Cao Q X, Zhu X X, et al. An automatic switching approach of robotic components for improving robot localization reliability in complicated environment. Ind Robot, 2014, 41: 135–144

  21. 21

    Winterhalter W, Fleckenstein F, Steder B, et al. Accurate indoor localization for RGB-D smartphones and tablets given 2D floor plans. In: Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg, 2015. 3138–3143

  22. 22

    Marinho L B, Almeida J S, Souza J W M, et al. A novel mobile robot localization approach based on topological maps using classification with reject option in omnidirectional images. Expert Syst Appl, 2017, 72: 1–17

  23. 23

    Xu D, Han L W, Tan M, et al. Ceiling-based visual positioning for an indoor mobile robot with monocular vision. IEEE Trans Ind Electron, 2009, 56: 1617–1628

  24. 24

    Farrell J. Aided Navigation: GPS With High Rate Sensors. New York: McGraw-Hill, 2008

Download references

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61725304, 61673361). The authors also gratefully acknowledge the support from Youth Top-notch Talent Support Program, 1000-talent Youth Program and Youth Yangtze River Scholar.

Author information

Correspondence to Yu Kang.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lv, W., Kang, Y. & Qin, J. FVO: floor vision aided odometry. Sci. China Inf. Sci. 62, 12202 (2019). https://doi.org/10.1007/s11432-017-9306-x

Download citation

Keywords

  • mobile robot
  • indoor localization
  • monocular vision
  • odometry
  • tile joint