Advertisement

RIDI: Robust IMU Double Integration

  • Hang YanEmail author
  • Qi Shan
  • Yasutaka Furukawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11217)

Abstract

This paper proposes a novel data-driven approach for inertial navigation, which learns to estimate trajectories of natural human motions just from an inertial measurement unit (IMU) in every smartphone. The key observation is that human motions are repetitive and consist of a few major modes (e.g., standing, walking, or turning). Our algorithm regresses a velocity vector from the history of linear accelerations and angular velocities, then corrects low-frequency bias in the linear accelerations, which are integrated twice to estimate positions. We have acquired training data with ground truth motion trajectories across multiple human subjects and multiple phone placements (e.g., in a bag or a hand). The qualitatively and quantitatively evaluations have demonstrated that our simple algorithm outperforms existing heuristic-based approaches and is even comparable to full Visual Inertial navigation to our surprise. As far as we know, this paper is the first to introduce supervised training for inertial navigation, potentially opening up a new line of research in the domain of data-driven inertial navigation. We will publicly share our code and data to facilitate further research (Project website: https://yanhangpublic.github.io/ridi).

Notes

Acknowledgement

This research is partially supported by National Science Foundation under grant IIS 1540012 and IIS 1618685, Google Faculty Research Award, and Zillow gift fund.

Supplementary material

Supplementary material 1 (mp4 75531 KB)

474201_1_En_38_MOESM2_ESM.pdf (85 kb)
Supplementary material 2 (pdf 84 KB)

References

  1. 1.
    Agarwal, S., Mierle, K., et al.: Ceres solver. http://ceres-solver.org
  2. 2.
  3. 3.
    Bahl, P., Padmanabhan, V.N.: RADAR: An in-building RF-based user location and tracking system. In: Proceedings of Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2000, vol. 2, pp. 775–784. IEEE (2000)Google Scholar
  4. 4.
    Brajdic, A., Harle, R.: Walk detection and step counting on unconstrained smartphones. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 225–234. ACM (2013)Google Scholar
  5. 5.
    Cadena, C., et al.: Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans. Robot. 32(6), 1309–1332 (2016)CrossRefGoogle Scholar
  6. 6.
    Chowdhary, M., Sharma, M., Kumar, A., Dayal, S., Jain, M.: Method and apparatus for determining walking direction for a pedestrian dead reckoning process. US Patent App. 13/682,684, 22 May 2014Google Scholar
  7. 7.
    Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: Monoslam: real-time single camera slam. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1052–1067 (2007)CrossRefGoogle Scholar
  8. 8.
    Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2018)CrossRefGoogle Scholar
  9. 9.
    Ferris, B., Fox, D., Lawrence, N.: WiFi-SLAM using Gaussian process latent variable models. In: Proceedings of IJCAI 2007, pp. 2480–2485 (2007)Google Scholar
  10. 10.
  11. 11.
  12. 12.
    Google: Project tango. https://get.google.com/tango/
  13. 13.
    Hesch, J.A., Kottas, D.G., Bowman, S.L., Roumeliotis, S.I.: Camera-IMU-based localization: observability analysis and consistency improvement. Int. J. Robot. Res. 33(1), 182–201 (2014)CrossRefGoogle Scholar
  14. 14.
    Huang, J., Millman, D., Quigley, M., Stavens, D., Thrun, S., Aggarwal, A.: Efficient, generalized indoor wifi graphslam. In: IEEE International Conference on Robotics and Automation, pp. 1038–1043 (2011)Google Scholar
  15. 15.
    Janardhanan, J., Dutta, G., Tripuraneni, V.: Attitude estimation for pedestrian navigation using low cost mems accelerometer in mobile applications, and processing methods, apparatus and systems. US Patent 8,694,251, 8 April 2014Google Scholar
  16. 16.
    Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: ISMAR, pp. 225–234. IEEE (2007)Google Scholar
  17. 17.
    Kourogi, M., Kurata, T.: A method of pedestrian dead reckoning for smartphones using frequency domain analysis on patterns of acceleration and angular velocity. In: 2014 IEEE/ION Position, Location and Navigation Symposium-PLANS 2014, pp. 164–168. IEEE (2014)Google Scholar
  18. 18.
    Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., Furgale, P.: Keyframe-based visual–inertial odometry using nonlinear optimization. Int. J. Robot. Res. 34(3), 314–334 (2015)CrossRefGoogle Scholar
  19. 19.
    Li, F., Zhao, C., Ding, G., Gong, J., Liu, C., Zhao, F.: A reliable and accurate indoor localization method using phone inertial sensors. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 421–430. ACM (2012)Google Scholar
  20. 20.
    Lim, C.H., Wan, Y., Ng, B.P., See, C.M.S.: A real-time indoor WiFi localization system utilizing smart antennas. IEEE Trans. Consum. Electron. 53(2), 618–622 (2007)CrossRefGoogle Scholar
  21. 21.
  22. 22.
    Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular slam system. IEEE Trans. Robot. 31(5), 1147–1163 (2015)CrossRefGoogle Scholar
  23. 23.
    Newcombe, R.A., Lovegrove, S.J., Davison, A.J.: DTAM: dense tracking and mapping in real-time. In: ICCV. pp. 2320–2327. IEEE (2011)Google Scholar
  24. 24.
    Racko, J., Brida, P., Perttula, A., Parviainen, J., Collin, J.: Pedestrian dead reckoning with particle filter for handheld smartphone. In: 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–7. IEEE (2016)Google Scholar
  25. 25.
  26. 26.
    Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Tian, Q., Salcic, Z., Kevin, I., Wang, K., Pan, Y.: An enhanced pedestrian dead reckoning approach for pedestrian tracking using smartphones. In: 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 1–6. IEEE (2015)Google Scholar
  28. 28.
    Yun, X., Bachmann, E.R., Moore, H., Calusdian, J.: Self-contained position tracking of human movement using small inertial/magnetic sensor modules. In: 2007 IEEE International Conference on Robotics and Automation, pp. 2526–2533. IEEE (2007)Google Scholar
  29. 29.
    Zhou, Q.Y., Koltun, V.: Simultaneous localization and calibration: self-calibration of consumer depth cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 454–460 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Washington University in St. LouisSt. LouisUSA
  2. 2.Zillow GroupSeattleUSA
  3. 3.Simon Fraser UniversityBurnabyCanada

Personalised recommendations