Moving Target Tracking with Robot Based on Laser-Camera Data Fusion

Abstract:

The aims of this paper is to promote object detection and tracking ability of mobile robot in outdoor unknown environments, which based on integration of camera and laser ranger finder data information. First, Camshift method is used to track area of target in image which observed by camera. Second, consistency grid map method is used to detect this moving target based on laser ranger finder observation. Next, two states of the target come from laser and camera sensors are fused to update error of estimation and the new state of target helps to determine search window of Camshift in next loop. Finally, a global optimization method is proposed to improve accuracy of laser-camera calibration. The experimental results show that the proposed algorithm is effective to track object in outdoor unknown environments.

• Periodical: Advanced Materials Research (Volume 1023)
• Pages: 141-144
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1023.141
• Online since: August 2014
• Authors: Ming Wu*, Lin Lin Li, Zhen Hua Wei, Hong Qiao Wang, Cheng Jian Li
• Keywords: Laser-Camera Data Fusion, Moving Target Tracking, SLAM

References

[1] C. C. Wang and C. Thorpe, Simultaneous Localization And Mapping with Detection And Tracking of Moving Objects. IEEE Int Conf on Robotics and Automarion, May, (2002).

[2] W. Ming, S. J Ying. Simultaneous Localization, Mapping and Detection of Moving Objects with Mobile Robot in Dynamic Environments. In Proc. of the IEEE int. Conf. on Computer Engineering and Technology. Chengdu, China, 2010. pp: 696-699.

DOI: 10.1109/iccet.2010.5485382

[3] W. Ming, S. J Ying. Extended Kalman Filter Based Moving Object Tracking by Mobile Robot in Unknown Environment. ROBOT, 2010, 32(3): 334-343.

DOI: 10.3724/sp.j.1218.2010.00334

[4] W. Ming, S. J Ying. Simultaneous Localization, Mapping and Detection of Moving Objects with Mobile Robot in Dynamic Environments. In Proc. of the IEEE int. Conf. on Computer Engineering and Technology. Chengdu, China, 2010. pp: 696-699.

DOI: 10.1109/iccet.2010.5485382

[5] Z. Qilong, R. Pless. Extrinsic calibration of a camera and laser range finder. In Proc. of the IEEE Int. Con. on Intelligent Robots and Systems, USA, 2004. pp: 1232-1238.

DOI: 10.1109/iros.2004.1389752

[6] D. Comaniciu, V. Ramesh, P. Meer. Kernel-based Object tracking. IEEE Transation on Pattern Analysis Machine Intelligence, 2003, 25(5): 564-577.

DOI: 10.1109/tpami.2003.1195991

[7] J.K. Uhlmann, Dynamic map building and localization for autonomous vehicles, Ph. D. Thesis, University of Oxford, (1995).

[8] T. Peynot. The Marulan Data Sets: Multi-Sensor Perception in Natural Environment with Challenging Conditions. International Journal of Robotics Research, 2010, 29(13): 1602-1607.

DOI: 10.1177/0278364910384638