针对无人机在无GPS环境和夜间环境下难以导航的问题，本文提出了一种基于异源图像匹配与惯导融合的机载自主定位与导航方法。该方法能实现红外或可见光航拍图像与卫星可见光基准图像的配准，并基于配准结果实现自主定位和航向修正。本文的异源图像匹配方法由基于结构特征的快速图像匹配和基于端到端学习网络的精确配准组成，前者可以提供20Hz左右的位置修正，后者可以提供高精度的位置和航向修正。本文通过位姿融合滤波方法，将图像匹配获得位置和方向信息与惯导融合，可以有效抑制惯性导航误差的发散，最终提高导航精度。挂飞测试结果表明本文方法比较现有方法能有效提高定位精度和可靠性，并能在机载嵌入式计算平台上达成实时处理。

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 guidance fusion. 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.