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Acta Aeronautica et Astronautica Sinica ›› 2026, Vol. 47 ›› Issue (10): 632116.doi: 10.7527/S1000-6893.2025.32116

• Special Issue: Intelligent Processing and Analysis of Aerospace Remote Sensing Images • Previous Articles    

Image matching based airborne inertial navigation system position and heading correction method

Jing DONG1, Quanfu HU1, Haiqiao LIU2, Songlai HAN1(), Yating YAO1, Zhikang CHEN1   

  1. 1.Research Institute of Aerospace Technology,Central South University,Changsha 410083,China
    2.College of Electrical and Information Engineering,Hunan Institute of Engineering,Xiangtan 411104,China
  • Received:2025-05-28 Revised:2025-06-27 Accepted:2025-09-03 Online:2025-09-12 Published:2025-09-10
  • Contact: Songlai HAN E-mail:songlai.han@csu.edu.cn
  • Supported by:
    National Key Research and Development Program of China(2023YFC2907000);National Natural Science Foundation of China(62203163);Hunan Provincial Natural Science Foundation(2025JJ60407)

Abstract:

To address the challenge of UAV navigation in GPS-free and nighttime environments, this paper proposes an airborne autonomous localization and navigation method based on heterogenous image matching and inertial navigationfusion. The proposed method enables the alignment of infrared or visible aerial images with geo-referenced satellite images, and subsequently realizes autonomous localization estimation and heading correction based on the alignment results. The heterogenous image matching method in this paper consists of a fast structural feature-based image matching module and an end-to-end learning-based fine registration network. The former provides position corrections at approximately 20 Hz, while the latter enables high-precision position and heading refinement. A pose fusion filtering strategy is employed to integrate the position and heading information obtained from image matching with inertial navigation data, effectively suppressing the drift in inertial systems and ultimately improving navigation accuracy. The flight test shows that the proposed method can effectively improve the positioning accuracy and reliability compared with existing methods, and can reach real-time processing on the airborne embedded computing platform.

Key words: heterogenous image matching, inertial navigation, end-to-end learning, fusion filter, autonomous localization

CLC Number: