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Improving Robustness of Vision Based Localization Under Dynamic Illumination

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Recent Advances in Robotics and Automation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 480))

Abstract

A dynamic light source poses significant challenges to vision based localization algorithms. There are a number of real world scenarios where dynamic illumination may be a factor, yet robustness to dynamic lighting is not demonstrated for most existing algorithms. Localization in dynamically illuminated environments is complicated by static objects casting dynamic shadows. Features may be extracted on both the static objects and their shadows, exacerbating localization error. This work investigates the application of a colour model which separates brightness from chromaticity to eliminate features and matches that may be caused by dynamic illumination. The colour model is applied in two novel ways. Firstly, the chromaticity distortion of a single feature is used to determine if the feature is the result of illumination alone. These features are removed before the feature matching process. Secondly, the chromaticity distortion of features matched between images is examined to determine if the monochrome based algorithm has matched them correctly. The evaluation of the techniques in a Simultaneous Localization and Mapping (SLAM) task show substantial improvements in accuracy and robustness.

Based on Vision Based Localization under Dynamic Illumination, by Jared Le Cras, Jonathan Paxman and Brad Saracik which appeared in the Proceedings of the 5th International Conference on Automation, Robotics and Applications (ICARA 2011). © 2011 IEEE.

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Authors and Affiliations

  1. Curtin University, Bentley, Western Australia, Australia

    Jared Le Cras, Jonathan Paxman & Brad Saracik

Authors
  1. Jared Le Cras
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  2. Jonathan Paxman
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  3. Brad Saracik
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Corresponding author

Correspondence to Jonathan Paxman .

Editor information

Editors and Affiliations

  1. School of Engineering &, Advanced Technology (SEAT), Massey University, Private Bag 11 222, Palmerston North, 4442, New Zealand

    Gourab Sen Gupta

  2. , School of Engineering and Advanced Techn, Massey University, Private Bag 11 222, Palmerston North, 4442, New Zealand

    Donald Bailey

  3. , Saigon South Campus, RMIT International University, Nguyen Van Linh City 702, Ho Chi Minh City, 70000, Vietnam

    Serge Demidenko

  4. , School of Engineering and Computer Scien, Victoria University of Wellington, Wellington, 6140, New Zealand

    Dale Carnegie

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Le Cras, J., Paxman, J., Saracik, B. (2013). Improving Robustness of Vision Based Localization Under Dynamic Illumination. In: Sen Gupta, G., Bailey, D., Demidenko, S., Carnegie, D. (eds) Recent Advances in Robotics and Automation. Studies in Computational Intelligence, vol 480. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37387-9_12

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  • DOI: https://doi.org/10.1007/978-3-642-37387-9_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37386-2

  • Online ISBN: 978-3-642-37387-9

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