Autonomous Robots

, Volume 40, Issue 5, pp 867–879 | Cite as

Humanoid odometric localization integrating kinematic, inertial and visual information

  • Giuseppe Oriolo
  • Antonio Paolillo
  • Lorenzo RosaEmail author
  • Marilena Vendittelli


We present a method for odometric localization of humanoid robots using standard sensing equipment, i.e., a monocular camera, an inertial measurement unit (IMU), joint encoders and foot pressure sensors. Data from all these sources are integrated using the prediction-correction paradigm of the Extended Kalman Filter. Position and orientation of the torso, defined as the representative body of the robot, are predicted through kinematic computations based on joint encoder readings; an asynchronous mechanism triggered by the pressure sensors is used to update the placement of the support foot. The correction step of the filter uses as measurements the torso orientation, provided by the IMU, and the head pose, reconstructed by a VSLAM algorithm. The proposed method is validated on the humanoid NAO through two sets of experiments: open-loop motions aimed at assessing the accuracy of localization with respect to a ground truth, and closed-loop motions where the humanoid pose estimates are used in real-time as feedback signals for trajectory control.


Humanoid robots Localization Odometry Visual SLAM EKF 

Supplementary material

Supplementary material 1 (mp4 20648 KB)


  1. Ahn, S., Yoon, S., Hyung, S., Kwak, N., & Roh, K. S. (2012). On-board odometry estimation for 3D vision-based SLAM of humanoid robot. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 4006–4012).Google Scholar
  2. Alcantarilla, P., Stasse, O., Druon, S., Bergasa, L., & Dellaert, F. (2013). How to localize humanoids with a single camera? Autonomous Robots, 34(1–2), 47–71.CrossRefGoogle Scholar
  3. Chestnutt, J., Takaoka, Y., Suga, K., Nishiwaki, K., Kuffner, J., & Kagami, S. (2009). Biped navigation in rough environments using on-board sensing. In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3543–3548).Google Scholar
  4. Davison, A. J. (2003). Real-time simultaneous localisation and mapping with a single camera. In 9th International Conference on Computer Vision (pp. 1403–1410).Google Scholar
  5. Davison, A. J., Reid, I. D., Molton, N. D., & Stasse, O. (2007). MonoSLAM: Real-time single camera SLAM. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 1052–1067.CrossRefGoogle Scholar
  6. Garrido-Jurado, S., Muñoz Salinas, R., Madrid-Cuevas, F. J., & Marín-Jiménez, M. J. (2014). Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognition, 47(6), 2280–2292.CrossRefGoogle Scholar
  7. Hernandez, E., Ibarra, J. M., Neira, J., Cisneros, R., & Lavín, J. E. (2011). Visual SLAM with oriented landmarks and partial odometry. In 21st IEEE International Conference on Electrical Communications and Computers (pp. 39–45).Google Scholar
  8. Hornung, A., Wurm, K. M., & Bennewitz, M. (2010). Humanoid robot localization in complex indoor environments. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 1690–1695).Google Scholar
  9. Hornung, A., Osswald, S., Maier, D., & Bennewitz, M. (2014). Monte carlo localization for humanoid robot navigation in complex indoor environments. International Journal of Humanoid Robotics, 11(02), 1441002.CrossRefGoogle Scholar
  10. Ido, J., Shimizu, Y., Matsumoto, Y., & Ogasawara, T. (2009). Indoor navigation for a humanoid robot using a view sequence. International Journal of Robotics Research, 28(2), 315–325.CrossRefGoogle Scholar
  11. Kelly, A. (2004). Fast and easy systematic and stochastic odometry calibration. In 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (Vol. 4, 3188–3194).Google Scholar
  12. Klein, G., & Murray, D. (2007). Parallel tracking and mapping for small AR workspaces. In 6th IEEE and ACM International Symposium on Mixed and Augmented Reality (pp. 225–234).Google Scholar
  13. Kwak, N., Stasse, O., Foissotte, T., & Yokoi, K. (2009). 3D grid and particle based SLAM for a humanoid robot. In 2009 9th IEEE-RAS International Conference on Humanoid Robots (pp. 62–67).Google Scholar
  14. Mombaur, K., Truong, A., & Laumond, J.-P. (2010). From human to humanoid locomotion—An inverse optimal control approach. Autonomous Robots, 28, 369–383.CrossRefGoogle Scholar
  15. Oriolo, G., Paolillo, A., Rosa, L., & Vendittelli, M. (2012) Vision-based odometric localization for humanoids using a kinematic EKF. In 2012 12th IEEE-RAS International Conference on Humanoid Robots (pp. 153–158).Google Scholar
  16. Oriolo, G., Paolillo, A., Rosa, L., & Vendittelli, M. (2013). Vision-based trajectory control for humanoid navigation. In 2013 13th IEEE-RAS International Conference on Humanoid Robots (pp. 118–123).Google Scholar
  17. Ozawa, R., Takaoka, Y., Kida, Y., Nishiwaki, K., Chestnutt, J., Kuffner, J., Kagami, J., Mizoguch, H., & Inoue, H. (2005). Using visual odometry to create 3D maps for online footstep planning. In 2005 IEEE International Conference on Systems, Man, and Cybernetics (Vol. 3, pp. 2643–2648).Google Scholar
  18. Pretto, A., Menegatti, E., Bennewitz, M., Burgard, W., & Pagello, E. (2009). A visual odometry framework robust to motion blur. In 2009 IEEE International Conference on Robotics and Automation (pp. 2250–2257).Google Scholar
  19. Samson, C. (1993). Time-varying feedback stabilization of car-like wheeled mobile robots. International Journal of Robotics Research, 12(1), 55–64.MathSciNetCrossRefGoogle Scholar
  20. Scaramuzza, D., & Fraundorfer, F. (2011). Visual odometry part I: The first 30 years and fundamentals. IEEE Robotics & Automation Magazine, 18(4), 80–92.CrossRefGoogle Scholar
  21. Stasse, O., Davison, A. C., Sellaouti, R., & Yokoi, K. (2006). Real-time 3D SLAM for a humanoid robot cosidering pattern generator information. In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 348–355).Google Scholar
  22. Takaoka, Y., Kida, Y., Kagami, S., Mizoguchi, H., & Kanade, T. (2004). 3D map building for a humanoid robot by using visual odometry. In 2004 IEEE International Conference on Systems, Man, and Cybernetics (Vol. 5, pp. 4444–4449).Google Scholar
  23. Tellez, R., Ferro, F., Mora, D., Pinyol, D., & Faconti, D. (2008). Autonomous humanoid navigation using laser and odometry data. In 2008 8th IEEE-RAS International Conference on Humanoid Robots (pp. 500–506).Google Scholar
  24. Thompson, S., Kagami, S., & Nishiwaki, K. (2006). Localisation for autonomous humanoid navigation. In 2006 IEEE-RAS International Conference on Humanoid Robots (pp. 13–19).Google Scholar
  25. Truong, T.-V.-A., Flavigne, D., Pettre, J., Mombaur, K., & Laumond, J.-P. (2010). Reactive synthesizing of human locomotion combining nonholonomic and holonomic behaviors. In 3rd IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (pp. 632–637).Google Scholar
  26. Weiss, S., & Siegwart, R. (2011). Real-time metric state estimation for modular vision-inertial systems. In 2011 IEEE International Conference on Robotics and Automation (pp. 4531–4537).Google Scholar
  27. Weiss, S., Scaramuzza, D., & Siegwart, R. (2011). Monocular-SLAM-based navigation for autonomous micro helicopters in GPS-denied environments. Journal of Field Robotics, 28(6), 854–874.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Giuseppe Oriolo
    • 1
  • Antonio Paolillo
    • 1
  • Lorenzo Rosa
    • 1
    Email author
  • Marilena Vendittelli
    • 1
  1. 1.Dipartimento di Ingegneria Informatica, Automatica e GestionaleSapienza Università di RomaRomeItaly

Personalised recommendations