Robust Subpixel Localization of Diagonal Markers in Large-Scale Navigation via Multi-Layer Screening and Adaptive Matching

TL;DR

This paper introduces a robust, high-precision localization method for diagonal markers in large-scale navigation, combining multi-layer screening and adaptive matching to improve accuracy and efficiency amidst complex backgrounds.

Contribution

It presents a novel three-tiered framework that reduces computational costs and enhances subpixel localization accuracy in large-scale navigation scenarios.

Findings

Effective in complex environments with background interference

Achieves subpixel precision through adaptive template matching

Reduces computational costs with a coarse-to-fine strategy

Abstract

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.