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The Effect of Pose on the Distribution of Edge Gradients in Omnidirectional Images

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Towards Autonomous Robotic Systems (TAROS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10965))

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Abstract

Images from omnidirectional cameras are used frequently in applications involving artificial intelligence and robotics as a source of rich information about the surroundings. A useful feature that can be extracted from these images is the distribution of gradients of the edges in the scene. This distribution is affected by the pose of the camera on-board a robot at any given location in the environment. This paper investigates the effect of the pose on this distribution. The gradients in the images are extracted and arranged into a histogram which is then compared to the histograms of other images using a chi-squared test. It is found that any differences in the distribution are not specific to either the position or orientation and that there is a significant difference in the distributions of two separate locations. This can aid in the localisation of robots when navigating.

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Notes

  1. 1.

    During the course of this paper the word “location” will refer to the immediate environment (the set of positions that share the same contextual name) of the robot for example the kitchen or the lab. Whereas “position” will be an exact measure of where the robot is (e.g. coordinates x, y). Pose defines the position (x, y) and orientation (theta) of the robot.

  2. 2.

    https://www.sony.co.uk/electronics/support/webbie-hd-bloggie-cameras-mhspm-series/mhs-pm5. Last accessed: 1/05/2018.

  3. 3.

    https://opencv.org/. Last accessed: 1/05/2018.

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Author information

Authors and Affiliations

  1. School of Computing and Mathematics, Keele University, Staffordshire, ST5 5BG, UK

    Dean Jarvis & Theocharis Kyriacou

Authors
  1. Dean Jarvis
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  2. Theocharis Kyriacou
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Corresponding author

Correspondence to Theocharis Kyriacou .

Editor information

Editors and Affiliations

  1. University of the West of England, Bristol, United Kingdom

    Manuel Giuliani

  2. University of Bath, Bath, United Kingdom

    Tareq Assaf

  3. University of Bristol, Bristol, United Kingdom

    Maria Elena Giannaccini

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Jarvis, D., Kyriacou, T. (2018). The Effect of Pose on the Distribution of Edge Gradients in Omnidirectional Images. In: Giuliani, M., Assaf, T., Giannaccini, M. (eds) Towards Autonomous Robotic Systems. TAROS 2018. Lecture Notes in Computer Science(), vol 10965. Springer, Cham. https://doi.org/10.1007/978-3-319-96728-8_20

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  • DOI: https://doi.org/10.1007/978-3-319-96728-8_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96727-1

  • Online ISBN: 978-3-319-96728-8

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