An Integrated Navigation Method Based on the Strapdown Inertial Navigation System/Scene-Matching Navigation System...

Area of Science:

• Robotics and Automation
• Navigation Systems
• Computer Vision

Background:

• Unmanned aerial vehicles (UAVs) face navigation challenges in Global Navigation Satellite System (GNSS)-denied environments.
• Discontinuous heterogeneous image matching and regional errors degrade navigation accuracy.
• Real-time performance is critical for UAV operations.

Purpose of the Study:

• To develop an integrated navigation method for UAVs overcoming GNSS-denied environment limitations.
• To improve the accuracy and reliability of UAV positioning.
• To enhance real-time navigation capabilities.

Main Methods:

• Designed a heterogeneous image-matching and positioning approach using infrared imagery.
• Developed a mathematical model for the integrated strapdown inertial navigation system (SINS)/scene-matching navigation system (SMNS).
• Utilized a Kalman filter (KF) for fusing SINS and SMNS data.

Main Results:

• The integrated SINS/SMNS method demonstrated superior horizontal positioning accuracy compared to standalone scene matching.
• Latitude accuracy improved by 52.34% and longitude accuracy by 45.54%.
• Achieved high-precision and reliable navigation positioning.

Conclusions:

• The proposed integrated SINS/SMNS navigation method effectively addresses UAV navigation challenges in GNSS-denied scenarios.
• This approach offers significant improvements in positioning accuracy and reliability.
• The method is suitable for real-time UAV navigation applications.