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Person Tracking by Detection of Mobile Robot using RGB-D Cameras

  • Kim, Young-Ju (Division of Computer Software Engineering, Silla University)
  • Received : 2017.11.28
  • Accepted : 2017.12.14
  • Published : 2017.12.29

Abstract

In this paper, we have implemented a low-cost mobile robot supporting the person tracking by detection using RGB-D cameras and ROS(Robot Operating System) framework. The mobile robot was developed based on the Kobuki mobile base equipped with 2's Kinect devices and a high performance controller. One kinect device was used to detect and track the single person among people in the constrained working area by combining point cloud data filtering & clustering, HOG classifier and Kalman Filter-based estimation successively, and the other to perform the SLAM-based navigation supported in ROS framework. In performance evaluation, the person tracking by detection was proved to be robustly executed in real-time, and the navigation function showed the accuracy with the mean distance error being lower than 50mm. The mobile robot implemented has a significance in using the open-source based, general-purpose and low-cost approach.

Keywords

References

  1. Siegwart, R., Nourbakhsh. I. R. and Scaramuzza, D., "Introduction to Autonomous Mobile Robots," 2nd Ed., The MIT Press, London, England, 2011.
  2. Yong-Seon Moon, Sang-Hyun Roh,Seung-Woo Lim and Young-Chul Bae, "An Implementation of the Control System of the Mobile Robot using ROS," The Journal of the Korea Institute of Electronic Communication Sciences, Vol.8, No.11, pp. 1713-1718, Nov. 2013. https://doi.org/10.13067/JKIECS.2013.8.11.1713
  3. Lee Hyun Sun and Jung Sl, "Implementation of Internet-based IoT Environment for Multiple Robot Systems," ICROS 2016, pp. 23-25, 2016.
  4. Doopalam Tuvshinjargal, Deok Jin Lee. "Efficient Kinect Sensor-Based Reactive Path Planning Method for Autonomous Mobile Robots in Dynamic Environments." Transactions of the Korean Society of Mechanical Engineers - A, 39.6, pp. 549-559, June 2015. https://doi.org/10.3795/KSME-A.2015.39.6.549
  5. T. Breuer, C. Bodensteiner, and M. Arens, "Low-cost Commodity Depth Sensor Comparison and Accuracy Analysis," in SPIE Security+Defence, vol. 9250. International Society for Optics and Photonics, 2014.
  6. A. M. Pinto, P. Costa, A. P. Moreira, L. F. Rocha, G. Veiga, and E. Moreira, "Evaluation of Depth Sensors for Robotic Applications," IEEE International Conference on Autonomous Robot Systems and Competitions(ICARSC), pp. 388-394, 2015.
  7. Kim Jun Sik, "RGB-D Camera Application Research Trend," Robot and Human, Vol.8, No.3, pp. 29-36, March 2011.
  8. Pyo Youn Seak, "ROS Robot Programming," Ruby Paper Publisher, 2015.
  9. ROS Web Site, http://www.ros.org/
  10. Litman, T., "Autonomous Vehicle Implementation Predictions: Implications for Transport Planning," Traffic Technology International, Victoria, Canada, pp. 36-42, June 2014.
  11. Van Den Berg, J. P. and Overmars, M. H., "Roadmap-based Motion Planning in Dynamic Environments," IEEE Trans. Robot, Vol. 21, No. 5, pp. 885-897, May 2005. https://doi.org/10.1109/TRO.2005.851378
  12. Simmons, R., "The Curvature-Velocity Method for Local Obstacle Avoidance," IEEE International Conference on Robotics and Automation, Vol. 4, pp. 3375-3382, 1996.
  13. N. Dalal and B. Triggs., "Histograms of oriented gradients for human detection," IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR 2005), Vol.1, pp. 886-893, July 2005.
  14. N. Bellotto and H. Hu. "Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of bayesian filters," Auton. Robots, Vol.28, pp. 425-438, Dec. 2009.
  15. F. Basso, M. Munaro, S. Michieletto, E. Pagello, and E. Menegatti, "Fast and robust multi-people tracking from RGB-D data for a mobile robot," In Proceedings of the 12th International Conference IAS-12, pp. 265-276, 2012.
  16. Linder T. and Arras K.O., "Multi-Model Hypothesis Tracking of Groups of People in RGB-D Data," IEEE Int. Conference on Information Fusion (FUSION'14), 2014.
  17. Jafari O. Hosseini, Mitzel D. and Leibe B.. "Real-Time RGB-D based People Detection and Tracking for Mobile Robots and Head-Worn Cameras," IEEE International Conference on Robotics and Automation (ICRA'14), 2014.
  18. Susperregi, Loreto, Martinez-Otzeta, Jose Maria, Ansuategi, Ander, Ibarguren, Aitor and Sierra, Basilio, "RGB-D, Laser and Thermal Sensor Fusion for People Following in a Mobile Robot," International Journal of Advanced Robotic Systems, Vol.10, pp. 5772-5780, Jan. 2013.
  19. M. Munaro and E. Menegatti, "Fast RGB-D people tracking for service robots," Journal on Autonomous Robots, Springer, vol. 37, no. 3, pp. 227-242, March 2014. https://doi.org/10.1007/s10514-014-9385-0
  20. PCL Web Site, http://pointclouds.org/
  21. SVM Library Web Site, https://www.csie.ntu.edu.tw/-cjlin/libsvm/
  22. kinect2_bridge Pakacge, https://github.com/code-iai/iai_kinect2