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Global Visual-Inertial Localization for Autonomous Vehicles with Pre-Built Map.

Yun Hao1, Jiacheng Liu1, Yuzhen Liu2

  • 1Department of Precision Instrument, Tsinghua University, Beijing 100084, China.

Sensors (Basel, Switzerland)
|May 13, 2023
PubMed
Summary

This study introduces a map-based localization method for autonomous vehicles that uses visual-inertial odometry and a pre-built map to achieve drift-free global pose estimation, outperforming existing techniques.

Keywords:
pre-built mapstate estimationvisual–inertial localization

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Area of Science:

  • Robotics
  • Computer Vision
  • Autonomous Systems

Background:

  • Accurate global pose estimation is crucial for autonomous vehicle navigation.
  • Existing visual-inertial localization methods often suffer from drift over time.

Purpose of the Study:

  • To develop a drift-free, map-based global localization method for autonomous vehicles.
  • To improve the accuracy and robustness of pose estimation by integrating visual-inertial odometry with a pre-built map.

Main Methods:

  • Proposed a novel global drift-free map-based localization approach.
  • Integrated visual-inertial odometry with global localization against a pre-built map.
  • Augmented the transformation between local odometry and global map frames into the state vector for online pose-graph optimization.

Main Results:

  • Demonstrated the effectiveness of the proposed method through extensive evaluations on public datasets and real-world experiments.
  • Achieved accurate global pose-estimation results across diverse scenarios.
  • The method proved more accurate and consistent compared to mainstream map-based localization techniques.

Conclusions:

  • The proposed method effectively eliminates drift in global pose estimation for autonomous vehicles.
  • The integration of visual-inertial odometry and map-based localization provides superior accuracy and consistency.
  • This approach offers a robust solution for reliable autonomous navigation.