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Learning and Vision-Based Obstacle Avoidance and Navigation

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Robot Intelligence

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

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Abstract

A novel algorithm for camera calibration and correction is proposed in this chapter. A model of camera distortion is built without any prior knowledge. The calibration parameters are obtained by optimizing an objective function about the sum of the back projection errors using the LM algorithm. Also the distorted images are corrected by using the LM algorithm. A comparative study based on both synthetic data and real images corrupted by noise shows that the proposed algorithm successfully calibrated and corrected the distorted image.

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Acknowledgements

The work in this chapter was supported in part by Natural Science Foundation of China (Grant Number: 60871078 and 60835004).

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Authors and Affiliations

    Authors
    1. Jiandong Tian
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    2. Yandong Tang
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    Correspondence to Jiandong Tian .

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    Editors and Affiliations

    1. School of Creative Technologies, The University of Portsmouth, Portsmouth, PO1 2DJ, United Kingdom

      Honghai Liu

    2. Department of Computer Science, University of Essex, Wivenhoe Park, United Kingdom

      Dongbing Gu

    3. , School of Engineering, University of Brighton, Moulsecoomb, Brighton, BN2 4GJ, United Kingdom

      Robert J. Howlett

    4. Department of Computer Science, University of Wales, Aberystwyth, SY23 3DB, United Kingdom

      Yonghuai Liu

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    Tian, J., Tang, Y. (2010). Learning and Vision-Based Obstacle Avoidance and Navigation. In: Liu, H., Gu, D., Howlett, R., Liu, Y. (eds) Robot Intelligence. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84996-329-9_7

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    • DOI: https://doi.org/10.1007/978-1-84996-329-9_7

    • Publisher Name: Springer, London

    • Print ISBN: 978-1-84996-328-2

    • Online ISBN: 978-1-84996-329-9

    • eBook Packages: Computer ScienceComputer Science (R0)

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