面向无人机绝对视觉定位的语义与结构约束分层图像匹配方法
Hierarchical Image Matching for UAV Absolute Visual Localization via Semantic and Structural Constraints
AI总结：
针对GNSS拒止场景下无人机跨源图像匹配定位难的问题，提出融合语义结构约束粗匹配与轻量化细匹配的分层方法，构建无相对定位依赖的视觉定位 pipeline，在公开及新数据集上实现更优精度与鲁棒性。
摘要：
绝对定位旨在确定智能体相对于全局参考系的位置，对各类应用中的无人机（UAV）至关重要，但在全球导航卫星系统（GNSS）信号不可用时会面临挑战。基于视觉的绝对定位方法通过在参考卫星地图中匹配无人机当前视图来估计位置，已在GNSS拒止场景中得到广泛应用。然而，现有方法大多依赖传统的低层图像匹配，受跨源差异和时序变化带来的显著图像差异影响，存在匹配困难的问题。\n为克服这些局限，本文提出一种面向无人机绝对定位的分层跨源图像匹配方法，该方法融合了语义感知与结构约束的粗匹配模块，以及轻量化的细粒度匹配模块。具体而言，在粗匹配模块中，由视觉基础模型提取的语义特征首先在语义与结构约束下建立区域级对应关系。随后，细粒度匹配模块用于提取精细特征并建立像素级对应关系。在此基础上，通过在提出的分层图像匹配模块前加入图像检索模块，构建了完全不依赖相对定位技术的无人机绝对视觉定位流程。\n在公开基准数据集与新提出的CS-UAV数据集上的实验评估表明，该方法在各类挑战性条件下具备更优的精度与鲁棒性，验证了其有效性。
英文摘要：
Absolute localization, aiming to determine an agent's location with respect to a global reference, is crucial for unmanned aerial vehicles (UAVs) in various applications, but it becomes challenging when global navigation satellite system (GNSS) signals are unavailable. Vision-based absolute localization methods, which locate the current view of the UAV in a reference satellite map to estimate its position, have become popular in GNSS-denied scenarios. However, existing methods mostly rely on traditional and low-level image matching, suffering from difficulties due to significant differences introduced by cross-source discrepancies and temporal variations. To overcome these limitations, in this paper, we introduce a hierarchical cross-source image matching method designed for UAV absolute localization, which integrates a semantic-aware and structure-constrained coarse matching module with a lightweight fine-grained matching module. Specifically, in the coarse matching module, semantic features derived from a vision foundation model first establish region-level correspondences under semantic and structural constraints. Then, the fine-grained matching module is applied to extract fine features and establish pixel-level correspondences. Building upon this, a UAV absolute visual localization pipeline is constructed without any reliance on relative localization techniques, mainly by employing an image retrieval module before the proposed hierarchical image matching modules. Experimental evaluations on public benchmark datasets and a newly introduced CS-UAV dataset demonstrate superior accuracy and robustness of the proposed method under various challenging conditions, confirming its effectiveness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
