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On-line Smoothing and Error Modelling for Integration of GNSS and Visual Odometry.

Thanh Trung Duong1, Kai-Wei Chiang2, Dinh Thuan Le2

  • 1Department of Geomatics and Land-administration, Hanoi University of Mining and Geology, Hanoi 122000, Vietnam.

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Summary

This study introduces a visual odometry (VO)/GNSS navigation system for challenging environments. The integrated system significantly improves positioning accuracy compared to VO alone, enabling reliable navigation where satellite signals are weak.

Keywords:
GNSSINSerror modellingintegrationnavigationon-line smoothingvisual odometry

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Area of Science:

  • Robotics and Autonomous Systems
  • Geomatics Engineering
  • Sensor Fusion

Background:

  • Global Navigation Satellite Systems (GNSS) are crucial for navigation but fail in signal-obstructed environments.
  • Visual Odometry (VO) offers an alternative but suffers from drift and inaccuracies.
  • Integrating GNSS with VO presents a solution for robust navigation.

Purpose of the Study:

  • To develop a real-time visual odometry (VO)/GNSS integrated navigation system.
  • To enhance navigation accuracy and reliability in GNSS-hostile conditions.
  • To compare the proposed system's efficiency against conventional methods.

Main Methods:

  • Proposed an on-line smoothing method using the Extended Kalman Filter (EKF) and Rauch-Tung-Striebel (RTS) smoother.
  • Developed VO error modeling for accurate error estimation and measurement compensation.
  • Conducted field tests in diverse GNSS-denied environments (e.g., tree canopy, urban areas).

Main Results:

  • The EKF-based data fusion reduced the root-mean-square error (RMSE) of 3D positioning by approximately 80 times compared to VO-only.
  • On-line smoothing and error modeling significantly improved accuracy, providing seamless navigation.
  • The proposed system demonstrated superior cost and accuracy efficiency compared to Inertial Navigation System (INS)/GNSS integration.

Conclusions:

  • The integrated VO/GNSS system effectively overcomes GNSS limitations in challenging environments.
  • The proposed EKF-based smoothing and error modeling enhance navigation precision and continuity.
  • This approach offers a more efficient and accurate solution for real-time navigation applications.