Visual tracking with sensing dynamics compensation using the Expectation-Maximization algorithm

Chung Yen Lin, Cong Wang, Masayoshi Tomizuka

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

6 Scopus citations

Abstract

Advances in vision-based technologies allow robots to perform sophisticated and intelligent tasks. Even with these advances, there still remain inherent problems with using vision-based technologies. Slow sampling rate and large latency is a problem associated with most vision hardware used in industry. We refer to these characteristics as the sensing dynamics associated with the vision sensor. This paper presents a compensation method that alleviates sensing dynamics issues in visual feedback tracking problems. We view the sensing dynamics compensation problem as two separate mathematical problems. Namely, we first deal with identifying the target model and then we deal with estimating the target position using the identified model and delayed measurements. The Expectation-Maximization algorithm and Kalman filtering are utilized to solve each problem respectively. The visual servo scheme associated with the proposed approach is also studied. Simulations and experiments are designed to test the performance capability of the proposed method.

Original languageEnglish (US)
Title of host publication2013 American Control Conference, ACC 2013
Pages6281-6286
Number of pages6
StatePublished - Sep 11 2013
Externally publishedYes
Event2013 1st American Control Conference, ACC 2013 - Washington, DC, United States
Duration: Jun 17 2013Jun 19 2013

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2013 1st American Control Conference, ACC 2013
CountryUnited States
CityWashington, DC
Period6/17/136/19/13

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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