通过多层筛选与自适应匹配，实现复杂环境下的高精度亚像素定位。
Robust Subpixel Localization of Diagonal Markers in Large-Scale Navigation via Multi-Layer Screening and Adaptive Matching
- 分三步：光照均衡+结构提取降维，粗到精候选筛选，自适应模板匹配
- 在复杂大场景中实现亚像素级定位，计算效率显著优于传统滑动窗口法
- 适合无人机等大范围导航中的角标记精准定位任务
本文提出一种鲁棒、高精度的定位方法，解决大规模飞行导航中因复杂背景干扰导致的定位失败问题，以及传统滑动窗口匹配方法固有的计算低效问题。该方法采用三层框架，包含多层角点筛选与自适应模板匹配。首先通过光照均衡和结构信息提取降低维度；其次采用粗到精的候选选择策略，大幅降低滑动窗口计算开销，实现标记位置的快速估计；最后为候选点生成自适应模板，通过相关系数极值拟合实现亚像素精度的模板匹配。实验表明，该方法在复杂大尺度环境中有效提取并定位对角标记，适用于导航任务中的视场测量。
原文摘要 · Abstract (English)
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.
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