This paper proposes a robust, high-precision positioning methodology to address localization failures arising from complex background interference in large-scale flight navigation and the computational inefficiency inherent in conventional sliding window matching techniques. The proposed methodology employs a three-tiered framework incorporating multi-layer corner screening and adaptive template matching. Firstly, dimensionality is reduced through illumination equalization and structural information extraction. A coarse-to-fine candidate selection strategy minimizes sliding window computational costs, enabling rapid estimation of the marker's position. Finally, adaptive templates are generated for candidate points, achieving subpixel precision through improved template matching with correlation coefficient extremum fitting. Experimental results demonstrate the method's effectiveness in extracting and localizing diagonal markers in complex, large-scale environments, making it ideal for field-of-view measurement in navigation tasks.

翻译：本文提出一种鲁棒、高精度的定位方法，以解决大规模飞行导航中因复杂背景干扰导致的定位失效问题，以及传统滑动窗口匹配技术固有的计算效率低下问题。所提出的方法采用三层框架，融合了多层角点筛选与自适应模板匹配。首先，通过光照均衡化与结构信息提取实现降维。采用由粗到细的候选点选择策略，最小化滑动窗口计算成本，从而快速估计标记位置。最后，为候选点生成自适应模板，通过改进的模板匹配与相关系数极值拟合实现亚像素级精度。实验结果表明，该方法在复杂、大规模环境中能有效提取并定位斜向标记，非常适用于导航任务中的视场测量。