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PHROG: A Multimodal Feature for Place Recognition.

Fabien Bonardi1, Samia Ainouz2, Rémi Boutteau3

  • 1Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes, Normandie University, UNIROUEN, UNIHAVRE, INSA Rouen, LITIS, 76000 Rouen, France. fabien.bonardi@litislab.fr.

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Summary
This summary is machine-generated.

This study introduces a new feature extraction method to improve place recognition across different spectral ranges, enhancing visual place recognition for diverse imaging sources like infrared and visible cameras.

Keywords:
cross-spectral imagingfeature extractionscene matchingvisual place recognition

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Area of Science:

  • Computer Vision
  • Robotics
  • Geospatial Analysis

Background:

  • Long-term place recognition in outdoor environments is challenging due to significant appearance changes.
  • Matching scenes across different spectral ranges (e.g., visible and infrared) adds complexity.
  • Existing feature point extractors struggle with repeatability across spectral ranges and long-term appearance variations.

Purpose of the Study:

  • To evaluate the performance of standard feature point extractors under long-term appearance changes and cross-spectral conditions.
  • To develop a novel feature extraction method that enhances repeatability across different spectral ranges.
  • To assess the robustness of feature extraction methods using diverse datasets from multiple imaging sources.

Main Methods:

  • Testing conventional feature point extractors on long-term datasets with varying appearance.
  • Developing and implementing a new feature extraction technique focused on cross-spectral repeatability.
  • Conducting evaluations using a Bag-of-Words approach on datasets including visible, Near InfraRed (NIR), Short Wavelength InfraRed (SWIR), and Long Wavelength InfraRed (LWIR) imagery.

Main Results:

  • Standard feature extractors show limitations in cross-spectral repeatability and long-term robustness.
  • The proposed feature extraction method demonstrates significant improvements in image retrieval for visual place recognition.
  • The method effectively handles the association of images from diverse spectral ranges, including infrared and visible light.

Conclusions:

  • The developed feature extraction method significantly enhances visual place recognition, especially when integrating data from different spectral ranges.
  • This approach offers a robust solution for long-term place recognition challenges in complex outdoor environments.
  • The findings are particularly relevant for applications requiring multi-spectral image fusion, such as autonomous navigation and surveillance.