Hierarchical Image Matching for UAV Absolute Visual Localization via Semantic and Structural Constraints
Journal:
arXiv
Published Date:
Jun 11, 2025
Abstract
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.
