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High-precision image aided inertial navigation with known features: observability analysis and performance

Weiping Jiang1, Li Wang2, Xiaoji Niu3

  • 1GNSS Research Center, Wuhan University, Wuhan 430079, China. wpjiang@whu.edu.cn.

Sensors (Basel, Switzerland)
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Summary

This study introduces an image-aided inertial navigation system (INS) as a reliable alternative to satellite navigation. It achieves centimeter-level positioning and half-degree attitude accuracy, even with limited visual data.

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Area of Science:

  • Robotics and Navigation
  • Computer Vision
  • Sensor Fusion

Background:

  • Satellite-based navigation systems like carrier-phase-based differential Global Navigation Satellite Systems (CDGNSSs) face limitations during signal outages.
  • Inertial Navigation Systems (INS) offer an alternative but require precise aiding for high accuracy.
  • Image-based navigation provides a potential solution for INS augmentation.

Purpose of the Study:

  • To propose and validate a high-precision image-aided inertial navigation system (INS).
  • To offer a robust alternative to CDGNSSs when satellite signals are unavailable.
  • To ensure reliable navigation performance even with limited visual feature points.

Main Methods:

  • Modeling the image/INS integrated algorithm using a tightly-coupled iterative extended Kalman filter (IEKF).
  • Developing a novel global observability analysis for the tightly-coupled integration.
  • Verifying the analysis and system performance through simulations and field tests.

Main Results:

  • The tightly-coupled IEKF ensures system reliability with fewer than three observed feature points.
  • Global observability analysis confirms system observability under necessary conditions.
  • Field tests demonstrate centimeter-level positioning and half-degree-level attitude accuracy.

Conclusions:

  • The proposed image-aided INS provides a reliable and high-precision navigation solution.
  • The system performs effectively even in environments with limited satellite visibility.
  • The tightly-coupled integration strategy and observability analysis are crucial for robust performance.