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

Hyeun Jeong Min, Nikolaos Papanikolopoulos, Christopher E. Smith, Vassilios Morellas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

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 (iii) 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.

Original languageEnglish (US)
Title of host publication2011 19th Mediterranean Conference on Control and Automation, MED 2011
Pages163-168
Number of pages6
DOIs
StatePublished - Sep 8 2011
Event2011 19th Mediterranean Conference on Control and Automation, MED 2011 - Corfu, Greece
Duration: Jun 20 2011Jun 23 2011

Publication series

Name2011 19th Mediterranean Conference on Control and Automation, MED 2011

Other

Other2011 19th Mediterranean Conference on Control and Automation, MED 2011
CountryGreece
CityCorfu
Period6/20/116/23/11

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