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2.5D Vision-Based Estimation

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Encyclopedia of Systems and Control

Abstract

2.5D vision-based techniques, also known as hybrid vision-based techniques, provide flexible ways to estimate the range or velocity of moving objects. The information from both the image space (2D) and the Cartesian space (3D) is simultaneously utilized to construct the system state in this technology, which overcomes the disadvantages of the traditional visual serving schemes. It has been widely adopted in Motion from structure, structure from motion, and structure and motion problems.

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Bibliography

  • Chaumette F, Hutchinson S (2006) Visual servo control part I: Basic approaches. IEEE Robot Autom Mag 13(4):82–90

    Article  Google Scholar 

  • Chen J, Dawson DM, Dixon WE, Behal A (2005) Adaptive homography-based visual servo tracking for a fixed camera configuration with a camera-in-hand extension. IEEE Trans Control Syst Technol 13(5):814–825

    Article  Google Scholar 

  • Chen J, Dawson DM, Dixon WE, Chitrakaran VK (2007) Navigation function-based visual servo control. Automatica 43(7):1165–1177

    Article  MathSciNet  Google Scholar 

  • Chen J, Chitrakaran VK, Dawson DM (2011) Range identification of features on an object using a single camera. Automatica 47(1):201–206

    Article  MathSciNet  Google Scholar 

  • Chen J, Zhang K, Jia B, Gao Y (2018) Identification of a moving object’s velocity and range with a static-moving camera system. IEEE Trans Autom Control 63(7):2168–2175

    Article  MathSciNet  Google Scholar 

  • Chitrakarana VK, Dawson DM, Dixon WE, Chen J (2005) Identification of a moving object’s velocity with a fixed camera. Automatica 41(3):553–562

    Article  MathSciNet  Google Scholar 

  • Chwa D, Dani AP, Dixon WE (2015) Range and motion estimation of a monocular camera using static and moving objects. IEEE Trans Control Syst Technol 24(4):1174–1183

    Article  Google Scholar 

  • Dani AP, Kan Z, Fischer NR, Dixon WE (2011) Structure estimation of a moving object using a moving camera: An unknown input observer approach. In: 50th IEEE conference on decision and control and European control conference, Orlando, pp 5005–5010

    Google Scholar 

  • Dani AP, Fischer NR, Dixon WE (2012) Single camera structure and motion. IEEE Trans Autom Control 57(1):241–246

    Article  MathSciNet  Google Scholar 

  • Hutchinson S, Hager GD, Corke PI (1996) A tutorial on visual servo control. IEEE Trans Robot Autom 12(5):651–670

    Article  Google Scholar 

  • Janabi-Sharifi F, Deng L, Wilson WJ (2011) Comparison of basic visual servoing methods. IEEE/ASME Trans Mechatron 16(5):967–983

    Article  Google Scholar 

  • Malis E, Chaumette F (1999) 2-1/2D visual servoing. IEEE Trans Robot Autom 15(2):238–250

    Google Scholar 

  • Malis E, Chaumette F (2000) 2 1/2 D visual servoing with respect to unknown objects through a new estimation scheme of camera displacement. Int J Comput Vis 37(1):79–97

    Article  Google Scholar 

  • Mariottini GL, Oriolo G, Prattichizzo D (2007) Image-based visual servoing for nonholonomic mobile robots using epipolar geometry. IEEE Trans Robot 23(1): 87–100

    Article  Google Scholar 

  • Parikh A, Cheng TH, Chen HY, Dixon WE (2017) A switched systems framework for guaranteed convergence of image-based observers with intermittent measurements. IEEE Trans Robot 33(2):266–280

    Article  Google Scholar 

  • Zhang K, Chen J, Li Y, Gao Y (2018) Unified visual servoing tracking and regulation of wheeled mobile robots with an uncalibrated camera. IEEE/ASME Trans Mechatron 23(4):1728–1739

    Article  Google Scholar 

  • Zhang K, Chaumette F, Chen J (2019) Trifocal tensor-based 6-DOF visual servoing. Int J Robot Res 38(10–11):1208–1228

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China

    Jian Chen

Authors
  1. Jian Chen
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Corresponding author

Correspondence to Jian Chen .

Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering, Boston University, Boston, MA, USA

    John Baillieul

  2. Automation and Control Solutions, Honeywell, Golden Valley, MN, USA

    Tariq Samad

Section Editor information

  1. Dept. of Mechanical and AeroMechanical and Aerospace Engineering, University of Florida, Room 334, MAE-B, Gainesville, FL, USA

    Warren Dixon

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Chen, J. (2020). 2.5D Vision-Based Estimation. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_100148-1

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  • DOI: https://doi.org/10.1007/978-1-4471-5102-9_100148-1

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5102-9

  • Online ISBN: 978-1-4471-5102-9

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