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A Multilayer Perceptron-Based Spherical Visual Compass Using Global Features.

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  • 1Université Bourgogne, 21000 Dijon, France.

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|April 13, 2024
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Summary
This summary is machine-generated.

This study introduces a visual compass using spherical moments and image masks to overcome challenges in robot navigation. The method accurately estimates rotation in real-time, suitable for embedded systems.

Keywords:
global feature extractionlocalizationmachine learningomnidirectional camerasrobot vision systemsrobots

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Area of Science:

  • Robotics
  • Computer Vision
  • Navigation Systems

Background:

  • Photometric methods for visual compasses face challenges with changing image regions and modeling translational motion effects on global features.
  • Scene depth variations, especially in non-planar environments, complicate the impact of translational motion on global features.

Purpose of the Study:

  • To develop a robust visual compass method using global features that addresses variations from changing image regions and translational motion.
  • To enable real-time rotation estimation for embedded robot applications.

Main Methods:

  • Utilizes spherical moments as global features for visual compassing.
  • Employs image masks to mitigate abrupt changes in global feature values caused by region appearance/disappearance.
  • Applies neural networks to model the impact of translational motion on global features.

Main Results:

  • The proposed method demonstrates accurate and robust rotation estimation.
  • Achieves real-time inference speeds, making it suitable for embedded robot applications.
  • Experimental results on real-world and synthetic datasets validate the method's performance against state-of-the-art techniques.

Conclusions:

  • The visual compass method effectively combines image masking and neural networks to overcome key challenges in photometric navigation.
  • The technique offers a rapid and efficient solution for real-time rotation estimation in robotic systems.