Specific Person Detection and Tracking by a Mobile Robot Using 3D LIDAR and ESPAR Antenna

  • Kazuki Misu
  • Jun Miura
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)


Tracking a specific person is one of the important tasks of mobile service robots. This paper describes a novel and reliable strategy for detecting and tracking a specific person by a mobile robot in outdoor environments. The robot uses 3D LIDARs for person detection and identification, and a directivity-variable antenna (called ESPAR antenna) for locating a specific person even under occluded and/or out-of-view situations. A sensor fusion approach realizes a reliable tracking of a specific person in actual outdoor environments. Experimental results show the effectiveness of the proposed strategy.


Person detection and tracking Mobile robot 3D LIDAR ESPAR antenna 



The authors would like to thank Prof. Takashi Ohira of TUT for giving advice on the ESPAR antenna. They would also like to thank the members of Active Intelligent Systems Laboratory at TUT for their supports in implementing the system. This work is supported in part by Grant-in-Aid for Scientific Research (No. 25280093) from JSPS.


  1. 1.
    N. Dalal and B. Briggs. Histograms of Oriented Gradients for Human Detection. In Proceedings of 2005 IEEE Conf. on Computer Vision and Patttern Recognition, pp. 886–893, 2005.Google Scholar
  2. 2.
    J. Satake, M. Chiba, and J. Miura. Visual Person Identification using a Distance-Dependent Appearance Model for a Person Following Robot. Int. J. of Automation and Computing, Vol. 10, No. 5, pp. 438–446, 2013.Google Scholar
  3. 3.
    H. Tsutsui, J. Miura, and Y. Shirai. Optical Flow-Based Person Tracking using Multiple Cameras. In Proceedings of the 2001 Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems, pp. 91–96, 2001.Google Scholar
  4. 4.
    A. Ess, B. Leibe, K. Schindler, and L.V. Gool. A Mobile Vision System for Robust Multi-Person Tracking. In Proceedings of the 2008 IEEE Conf. on Computer Vision and Pattern Recognition, 2008.Google Scholar
  5. 5.
    D. Schulz, W. Burgard, D. Fox, and A.B. Cremers. People Tracking with a Mobile Robot Using Sample-Based Joint Probabilistic Data Association Filters. Int. J. of Robotics Research, Vol. 22, No. 2, pp. 99–116, 2003.Google Scholar
  6. 6.
    K.O. Arras, O.M. Mozos, and W. Burgard. Using Boosted Features for the Detection of People in 2D Range Data. In Proceedings of the 2007 IEEE Int. Conf. on Robotics and Automation, pp. 3402–3407, 2007.Google Scholar
  7. 7.
    C. Premebida, O. Ludwig, and U. Nunes. Exploiting LIDAR-based Features on Pedestrian Detection in Urban Scenarios. In Proceedings of the 12th IEEE Int. Conf. on Intelligent Transportation Systems, pp. 18–23, 2009.Google Scholar
  8. 8.
    Z. Zainudin, S. Kodagoda, and G. Dissanayake. Torso Detection and Tracking using a 2D Laser Range Finder. In Proceedings of Australasian Conf. on Robotics and Automation 2010, 2010.Google Scholar
  9. 9.
    L. Spinello, K.O. Arras, R. Triebel, and R. Siegwart. A Layered Approach to People Detection in 3D Range Data. In Proceedings of the 24th AAAI Conf. on Artificial Intelligence, pp. 1625–1630, 2010.Google Scholar
  10. 10.
    L.E. Navarro-Serment, C. Mertz, and M. Hebert. Pedestrian Detection and Tracking using Three-Dimensional LADAR Data. Int. J. of Robotics Research, Vol. 29, No. 12, pp. 1516–1528, 2010.Google Scholar
  11. 11.
    K. Kidono, T. Miyasaka, A. Watanabe, T. Naito, and J. Miura. Pedestrian Recognition Using High-Definition LIDAR. In Proceedings of 2011 IEEE Intelligent Vehicles Symp., pp. 405–410, 2011.Google Scholar
  12. 12.
    J. Satake, M. Chiba, and J. Miura. A SIFT-based Person Identification using a Distance-Dependent Appearance Model for a Person Following Robot. In Proceedings of the 2012 IEEE Int. Conf. on Robotics and Biomimetics, pp. 962–967, 2012.Google Scholar
  13. 13.
    P.N. Belhumer, J.P. Hespanha, and D.J. Kriegman. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 711–720, 1997.Google Scholar
  14. 14.
    K. Sugiura, Y. Makihara, and Y. Yagi. Gait Identification based on Multi-view Observations using Omnidirectional Camera. In Proceedings of 8th Asian Conf. on Computer Vision, Vol. 1, pp. 452–461, 2007.Google Scholar
  15. 15.
    T. Germa, F. Lerasle, N. Ouadah, and V. Cadenat. Vision and RFID Data Fusion for Tracking People in Crowds by a Mobile Robot. Computer Vision and Image Understanding, Vol. 114, No. 6, pp. 641–651, 2010.Google Scholar
  16. 16.
    H. Kawakami and T. Ohira. Electrically Steerable Passive Array Radiator (ESPAR) Antennas. IEEE Antennas and Propagation Magazine, Vol. 47, No. 2, pp. 43–50, 2005.Google Scholar
  17. 17.
    N. Ando, T. Suehiro, and T. Kotoku. A Software Platform for Component Based RT System Development: OpenRTM-aist. In Proceedings of the 1st Int. Conf. on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR ’08), pp. 87–98, 2008.Google Scholar
  18. 18.
  19. 19.
    I. Ardiyanto and J. Miura. Real-Time Navigation using Randomized Kinodynamic Planning with Arrival Time Field. Robotics and Autonomous Systems, Vol. 60, No. 12, pp. 1579–1591, 2012.Google Scholar
  20. 20.
    G.T. Toussaint. Solving Geometric Problems with the Rotating Calipers. In IEEE Mediterranean Electrotechnical Conf., pp. 1–8, 1983.Google Scholar
  21. 21.
    R.E. Schapire and Y. Singer. Improved Boosting Algorithms Using Confidence-Rated Projections. Machine Learning, Vol. 37, No. 3, pp. 297–336, 1999.Google Scholar
  22. 22.
    G. Welch and G. Bishop. An Introduction to the Kalman Filter. Technical Report TR 95–041, Department of Computer Science, University of North Carolina at Chapel Hill, 1995.Google Scholar
  23. 23.
    H. Grabner and H. Bischof. On-line Boosting and Vision. In Proceedings of IEEE Conf. on Computer Vision and Pattern Recognition, Vol. 1, pp. 260–267, 2006.Google Scholar
  24. 24.
    S. Jurić-Kavelj, I. Marković, and I. Petrović. People Tracking with Heterogeneous Sensors using JPDAF with Entropy Based Track Management. In Proceedings of the 5th European Conf. on Mobile Robots, pp. 31–36, 2011.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringToyohashi University of TechnologyToyohashiJapan

Personalised recommendations