A Low-Cost Stereo Vision System for Real-Time Pose Estimation and its Application for Robot Tracking

Abstract:

In this paper, a 3-D pose estimation system by using stereo vision with low-cost devices is presented. It is developed as a base system for application development. Two webcams and a planar target with circular markers are used to reduce development cost and computational complexity. To avoid correspondence search problem, user has to select regions of interest (ROI’s) of each marker on the two images in the same sequence before starting the 3-D reconstruction process. Linear triangulation method is applied for 3-D position calculation of each marker. These positions and the positions of the markers referenced in the planar target coordinate frame are used for pose estimation by using least-squares fitting algorithm to obtain the position and orientation of the planar target. The system can be applied for robot tracking as shown in the experiments. The experimental results validate the system’s ability to estimate object pose in real-time with minimum system frequency of 25 Hz.

• Periodical: Applied Mechanics and Materials (Volume 619)
• Pages: 249-253
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.619.249
• Online since: August 2014
• Authors: Viboon Sangveraphunsiri*, Pongsakon Bamrungthai
• Keywords: Marker-Based Tracking, Pose Estimation, Stereo Vision System

References

[1] T. Pintaric, and H. Kaufmann: Affordable Infrared-Optical Pose-Tracking for Virtual and Augmented Reality, in Proceedings of Trends and Issues in Tracking for Virtual Environments Workshop, IEEE VR 2007, Charlotte, NC, USA, (2007), pp.44-51.

[2] P. Santos, A. Stork, A. Buaes, C.E. Pereira, and J. Jorge: A real-time low-cost marker-based multiple camera tracking solution for virtual reality applications, J. Real-Time Image Processing 5(2) (2010): 121-128.

DOI: 10.1007/s11554-009-0138-9

[3] R. Poppe: Vision-based human motion analysis: An overview, Computer Vision and Image Understanding, 108 (1-2) (2007), pp.4-18.

DOI: 10.1016/j.cviu.2006.10.016

[4] A. Oltramari, and C. Lebiere: Using Ontologies in a Cognitive-Grounded System: Automatic Action Recognition in Video Surveillance, in Proceedings of the 7th International Conference on Semantic Technology for Intelligence, Defense, and Security, Fairfax, VA (2012).

[5] C. -L. Huang, and C. -Y. Chung: A Real-Time Model-Based Human Motion Tracking and Analysis for Human-Computer Interface Systems, EURASIP J. Adv. Sig. Proc., 2004(11) (2004): 1648-1662.

DOI: 10.1155/s1110865704401206

[6] R.C. Gonzalez, and R.E. Woods: Digital image processing, 3rd edition, Upper Saddle River, New Jersey, USA: Prentice Hall (2008).

[7] F. Chang, C.J. Chen, and C.J. Lu: A Linear-Time Component-Labeling Algorithm using Contour Tracing Technique, Comput. Vis. Image Underst, 93(2) (2004), 206-220.

DOI: 10.1016/j.cviu.2003.09.002

[8] R. Hartley, and A. Zisserman: Multiple view geometry in computer vision, 2nd edition, Cambridge, UK: Cambridge University Press (2003).

[9] K.S. Arun, T.S. Huang, and S.D. Blostein: Least-squares fitting of two 3-D point sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(5) (1987): 699-700.

DOI: 10.1109/tpami.1987.4767965