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Feature-based covariance matching for a moving target in multi-robot following

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4 Author(s)
Hyeun Jeong Min ; Department of Computer Science and Engineering, University of Minnesota, Minneapolis, 55455, USA ; Nikolaos Papanikolopoulos ; Christopher E. Smith ; Vassilios Morellas

In this work we present a moving target segmentation technique and apply it to a vision-based robot following problem. The capability to do autonomous multi-robot following is useful for many robot-team applications; however, the problem becomes very challenging when the robots can carry only a small camera or when they exhibit unpredictable motion. The ability to segment a moving target while the camera is also in motion is critical to the solution of this problem and is the focus of our work. Our contributions include: (i) Matching targets using feature-based covariance matrices; (ii) Enhancing matching performance by using features based upon the Fourier transform; and (Hi) Initializing a target model for cases without a known target model. We compare the proposed method with the scale-invariant feature transform and existing covariance matching methods. We then validate our proposed segmentation method through real-robot experiments.

Published in:

Control & Automation (MED), 2011 19th Mediterranean Conference on

Date of Conference:

20-23 June 2011