Author : Jennifer Jacob 1
Date of Publication :8th March 2017
Abstract: Mobile robotics is a fast developing area due to their wide applications in exploration on-ground, under-ground, on-water, under-water and space. Detection and tracking of the paths followed by these robots are required to analyze their motion and to guide them through optimal path. This paper focuses on the application of image-processing for estimating the position and velocity of mobile robot on an indoor workspace. The motion of the robot on the workspace was captured using monocular vision system. In order to detect the path of the mobile robot, the obtained image was processed using a full featured high-level programming language-MATLAB. This work is fully dependent on how well the feature of the robot in the image plane was extracted and tracked from the initial frame to the subsequent frames. Thus, color-based feature extraction and blob analysis are used to serve the purpose of detecting the path of the mobile robot. The resulting data was further used to obtain path coordinates which in turn gives the position and velocity errors of the robot. A calibration phase was carried out to analyze the relationship between the measured coordinates (pixels) with the world coordinates. Some of the challenges faced during implementation of the system are also mentioned in the paper.
Reference :
-
- A. Cavallaro and T. Ebrahimi, “Interaction between high-level and low-level image analysis for semantic video object extraction”, EURASIP Journal on Applied Signal Processing, vol. 6, pp. 786-797, June 2004.
- C. Stauffer and W. E. L. Grimson, “Learning patterns of activity using real-time tracking, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, pp. 747-757, 2000.
- D. Comaniciu, V. Ramesh and P. Meer, “Kernel-based object tracking”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, pp. 564-577, May 2003.
- V. Parameswaran, V. Ramesh and I. Zoghlami, “Tunable kernels for tracking,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2179-2186, 2006.
- Y. Raja, S. J. Mckenna and S. Gong, “Segmentation and tracking using color mixture models”, Proceedings of Third Asian Conference on Computer Vision, vol. 1, pp.607-614, Jan 1998.
- N. Paragios and R. Deriche, “Unifying boundary and region-based information for geodesic active tracking”, IEEE Computer Vision and Pattern Recognition, 1999.
- Al. Bovik, The essential guide to video processing. AP, 2009.