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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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Deep learning-based robust positioning for all-weather autonomous driving.

Yasin Almalioglu1, Mehmet Turan2, Niki Trigoni1

  • 1Department of Computer Science, University of Oxford, Oxford, UK.

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This study introduces a deep learning method for self-driving cars to estimate their location accurately, even in bad weather. This approach fuses visual and radar data for robust all-weather autonomous driving.

Keywords:
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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Autonomous vehicles (AVs) face challenges in precise localization due to sensor limitations in adverse weather.
  • Current AV deployment is restricted to small-scale trials, hindering widespread adoption.
  • Accurate ego-motion estimation is critical for safe and reliable AV operation.

Purpose of the Study:

  • To develop a robust ego-motion estimation method for autonomous vehicles.
  • To improve AV localization capabilities under inclement weather conditions.
  • To create a complementary localization solution that overcomes sensor imperfections.

Main Methods:

  • A deep learning-based, self-supervised approach for ego-motion estimation.
  • Geometry-aware fusion of visual and radar sensor data using attention-based learning.
  • Prediction of reliability masks to mitigate multimodal data deficiencies.

Main Results:

  • Demonstrated robust all-weather performance in rain, fog, and snow.
  • Showcased effective cross-domain generalizability across various conditions (day/night, harsh weather).
  • Utilized game theory for interpretability, revealing independent failure modes in the multimodal system.

Conclusions:

  • The proposed method offers a reliable localization solution for autonomous vehicles in adverse weather.
  • This approach enhances the safety and dependability of AVs for all-weather autonomous driving.
  • The fusion of visual and radar data with reliability masking proves effective for robust navigation.