DOI QR코드

DOI QR Code

Position Improvement of a Mobile Robot by Real Time Tracking of Multiple Moving Objects

실시간 다중이동물체 추적에 의한 이동로봇의 위치개선

  • Published : 2008.04.25

Abstract

The Intelligent Space(ISpace) provides challenging research fields for surveillance, human-computer interfacing, networked camera conferencing, industrial monitoring or service and training applications. ISpace is the space where many intelligent devices, such as computers and sensors, are distributed. According to the cooperation of many intelligent devices, the environment, it is very important that the system knows the location information to offer the useful services. In order to achieve these goals, we present a method for representing, tracking and human Jollowing by fusing distributed multiple vision systems in ISpace, with application to pedestrian tracking in a crowd. This paper describes appearance based unknown object tracking with the distributed vision system in intelligent space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.

본 논문은 실내외 공간에서 인간을 포한함 이동물체의 영상정보를 이용하여 이동로봇의 자기위치를 인식하기 위한 방법을 제시하고 있다. 제시한 방법은 로봇자체의 DR센서 정보와 카메라에서 얻은 영상정보로부터 로봇의 위치추정방법을 결합한 것이다. 그리고 이동물체의 이전 위치정보와 관측 카메라의 모델을 사용하여 이동물체에 대한 영상프레임 좌표와 추정된 로봇위치간의 관계를 표현할 수 있는 식을 제시하고 있다. 또한 이동하는 인간과 로봇의 위치와 방향을 추정하기 위한 제어방법을 제시하고 이동로봇의 위치를 추정하기 위해서 칼만필터 방법을 적용하였다. 그리고 시뮬레이션 및 실험을 통하여 제시한 방법을 검증하였다.

Keywords

References

  1. A. Georgiev, P. K. Allen, "Vision for mobile robot localization in urban environments," Intelligent Robots and System, IEEE/RSJ Int. Conference on, vol.1, pp. 472-477, Sept. 2002
  2. A. Georgiev, P. K. Allen, "Vision for mobile robot localization in urban environments," Intelligent Robots and System, IEEE/RSJ Int. Conference on, vol.1, pp. 472-477, Sept. 2002
  3. S. I. Roumeliotis, G. A. Bekey, "Bayesian estimation and Kalman filtering: a unified framework for mobile robot localization," Robotics and Automation, Proc. ICRA. IEEE International Conference on, vol. 3, pp. 2985-2992, April 2000
  4. Robert M. Haralick and Linda G. Shapiro, Computer and Robot Vision, Addison-Wesley, 1993
  5. H. W. Sorenson, "Kalman Filtering Techniques," Advances in Control Systems Theory and Applications, vol. 3, pp. 219-292, 1966
  6. B. Jung and G. S. Sukhatme, "Tracking targets using multiple robots: The effect of environment occlusion," Autonomous Robots, vol. 13, no. 3, pp. 191-205, 2002 https://doi.org/10.1023/A:1020598107671
  7. M. Mazo, A. Speranzon, K. Johansson, and X. Hu, "Multi-robot tracking of a moving object using directional sensors," in IEEE Int. Conf. on Robotics and Automation, New Orleans, LA, April 2004, pp. 1103-1108
  8. A. Georgiev and P. K. Allen, "Localization methods for a mobile robot in urban environments," IEEE Trans. on Robotics, vol. 20, no. 5, pp. 851-864, October 2004 https://doi.org/10.1109/TRO.2004.829506
  9. S. Pfister, S. Roumeliotis, and J. Burdick, "Weighted line fitting algorithms for mobile robot map building and efficient data representation," in Proceedings of the IEEE Int. Conf. on Robotics and Automation, Taipei, Taiwan, Sep. 14-19 2003, pp. 1304-1311
  10. A. I. Mourikis and S. I. Roumeliotis, "Performance bounds for cooperative simultaneous localization and mapping (C-SLAM)," in Proc. of Robotics: Science and Systems, Cambridge, MA, June 2005, pp. 73-80
  11. F. M. Mirzaei, A. I. Mourikis, and S. I. Roumeliotis, "Analysis of positioning uncertainty in cooperative localization and target tracking (CLATT)," Dept. of Comp. Sci., Univ. of Minnesota, Tech. Rep., 2005