Object Detection as an Optional Basis: A Graph Matching Network for Cross-View UAV Localization

TL;DR

This paper introduces a novel cross-view UAV localization method that uses object detection and graph neural networks to improve map matching accuracy in GNSS-denied environments, demonstrating strong results on multiple datasets.

Contribution

The paper proposes a new framework combining object detection and graph neural networks for cross-view UAV localization, addressing heterogeneity and modality gaps effectively.

Findings

Achieves high localization accuracy on public and real-world datasets.

Effectively handles heterogeneous appearance differences and modality gaps.

Generalizes well to infrared-visible image matching scenarios.

Abstract

With the rapid growth of the low-altitude economy, UAVs have become crucial for measurement and tracking in patrol systems. However, in GNSS-denied areas, satellite-based localization methods are prone to failure. This paper presents a cross-view UAV localization framework that performs map matching via object detection, aimed at effectively addressing cross-temporal, cross-view, heterogeneous aerial image matching. In typical pipelines, UAV visual localization is formulated as an image-retrieval problem: features are extracted to build a localization map, and the pose of a query image is estimated by matching it to a reference database with known poses. Because publicly available UAV localization datasets are limited, many approaches recast localization as a classification task and rely on scene labels in these datasets to ensure accuracy. Other methods seek to reduce cross-domain differences using polar-coordinate reprojection, perspective transformations, or generative adversarial networks; however, they can suffer from misalignment, content loss, and limited realism. In contrast, we leverage modern object detection to accurately extract salient instances from UAV and satellite images, and integrate a graph neural network to reason about inter-image and intra-image node relationships. Using a fine-grained, graph-based node-similarity metric, our method achieves strong retrieval and localization performance. Extensive experiments on public and real-world datasets show that our approach handles heterogeneous appearance differences effectively and generalizes well, making it applicable to scenarios with larger modality gaps, such as infrared-visible image matching. Our dataset will be publicly available at the following URL: https://github.com/liutao23/ODGNNLoc.git.