Indoor Real-Time Localisation for Multiple Autonomous Vehicles Fusing Vision, Odometry and IMU Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9991)


Due to the increasing usage of service and industrial autonomous vehicles, a precise localisation is an essential component required in many applications, e.g. indoor robot navigation. In open outdoor environments, differential GPS systems can provide precise positioning information. However, there are many applications in which GPS cannot be used, such as indoor environments. In this work, we aim to increase robot autonomy providing a localisation system based on passive markers, that fuses three kinds of data through extended Kalman filters. With the use of low cost devices, the optical data are combined with other robots’ sensor signals, i.e. odometry and inertial measurement units (IMU) data, in order to obtain accurate localisation at higher tracking frequencies. The entire system has been developed fully integrated with the Robotic Operating System (ROS) and has been validated with real robots.


Localisation indoor Odometry IMU EKF Passive marker 


  1. 1.
    O’Kane, J.M.: Global localization using odometry. In: Proceedings IEEE International Conference on Robotics and Automation, ICRA, pp. 37–42. IEEE (2006)Google Scholar
  2. 2.
    Malyavej, V., Kumkeaw, W., Aorpimai, M.: Indoor robot localization by RSSI/IMU sensor fusion. In: 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 1–6. IEEE (2013)Google Scholar
  3. 3.
    Pereira, G.A.S., Vijay Kumar,R., Campos, M.F.M.: Localization and tracking in robot networks. In: Proceedings of the 11th International Conference on Advanced Robotics (ICAR), Coimbra, Portugal, 30 June–3 July 2003Google Scholar
  4. 4.
    Panzieri, S., Pascucci, F., Setola, R., Ulivi, G.: A low cost vision based localization system for mobile robots. Target 4, 5 (2001)Google Scholar
  5. 5.
    Bayramolu, E., Andersen, N.A., Poulsen, N.K., Andersen, J.C., Ravn, O.: Mobile robot navigation in a corridor using visual odometry. In: International Conference on Advanced Robotics: ICAR 2009, pp. 1–6. IEEE (2009)Google Scholar
  6. 6.
    Fu, G., Zhang, J., Chen, W., Peng, F., Yang, P., Chen, C.: Precise localization of mobile robots via odometry and wireless sensor network. Int. J. Adv. Robot. Syst. 10 (2013)Google Scholar
  7. 7.
    Baatar, G., Eichhorn, M., Ament, C.: Precise indoor localization of multiple mobile robots with adaptive sensor fusion using odometry and vision data. In: The International Federation of Automatic Control Cape Town, South Africa (2014)Google Scholar
  8. 8.
    Nardi, S., Della Santina, C., Meucci, D., Pallottino, L.: Coordination of unmanned marine vehicles for asymmetric threats protection. In: OCEANS Genova. IEEE (2015)Google Scholar
  9. 9.
    Nardi, S., Fabbri, T., Caiti, A., Pallottino, L.: A game theoretic approach for antagonistic-task coordination of underwater autonomous robots in asymmetric threats scenarios. In: OCEANS Monterey. IEEE (2016)Google Scholar
  10. 10.
    Nardi, S., Pallottino, L.: NoStop: a real-time framework for design and test of coordination protocol for unmanned marine vehicles involved in asymmetric threats protection. In: Hodicky, J. (ed.) MESAS 2016. LNCS, vol. 9991, pp. 176–185. Springer, Heidelberg (2016)Google Scholar
  11. 11.
    Bischoff, B., Nguyen-Tuong, D., Streichert, F., Ewert, M., Knoll, A.: Fusing vision and odometry for accurate indoor robot localization. In: 12th International Conference on Control Automation Robotics and Vision (ICARCV), pp. 347–352. IEEE (2012)Google Scholar
  12. 12.
    Forster, C., Pizzoli, M., Scaramuzza, D.: SVO: fast semi-direct monocular visual odometry. In: IEEE International Conference on Robotics and Automation (ICRA) (2014)Google Scholar
  13. 13.
    Szeliski, R.: Computer Vision: Algorithms and Applications. Springer Science and Business Media, London (2010)zbMATHGoogle Scholar
  14. 14.
    Lobo, A., Kadam, R., Shajahan, S., Malegam, K., Wagle, K., Surve, S.: Localization and tracking of indoor mobile robot with beacons and dead reckoning sensors. In: IEEE Students Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1–4. IEEE (2014)Google Scholar
  15. 15.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3, p. 5 (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Research Center E. Piaggio, Faculty of EngineeringUniversity of PisaPisaItaly

Personalised recommendations