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
Country/TerritoryGreece
CityCorfu
Period6/20/116/23/11

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