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A Robust Rotation-Equivariant Feature Extraction Framework for Ground Texture-Based Visual Localization.

Yuezhen Cai1, Linyuan Xia1, Ting On Chan1

  • 1School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Ground Texture Rotation-Equivariant Keypoints and Descriptors (GT-REKD), a novel framework for robust visual localization. GT-REKD significantly improves pose estimation accuracy under challenging rotations and sparse textures.

Keywords:
deep learningfeature extractionground texturekeypoint detectionvisual localization

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Ground texture-based localization offers robust pose estimation but struggles with large rotations and sparse textures.
  • Traditional feature extraction methods lack reliability in challenging ground texture scenarios.

Purpose of the Study:

  • To develop a learning-based feature extraction framework, GT-REKD, to overcome limitations in ground texture-based localization.
  • To achieve rotation-invariant keypoint and descriptor extraction for enhanced localization accuracy.

Main Methods:

  • Implemented a learning-based framework (GT-REKD) utilizing group-equivariant convolutions over the cyclic rotation group.
  • Incorporated directional attention and orientation-encoding heads for precise feature extraction.
  • Generated dense keypoints and descriptors invariant to 0-360° in-plane rotations.

Main Results:

  • GT-REKD achieved 96.14% matching in pure rotation tests and 94.08% in incremental localization.
  • Demonstrated low relocalization errors of 5.55° and 4.41 pixels.
  • Consistently outperformed baseline methods under extreme rotations and sparse textures.

Conclusions:

  • GT-REKD provides a robust solution for visual localization challenges posed by rotations and sparse textures.
  • The framework shows significant applicability to visual localization and simultaneous localization and mapping (SLAM) tasks.