UltraVPR: Unsupervised Lightweight Rotation- Invariant Aerial Visual Place Recognition

Aerial Visual Place Recognition (VPR) is critical for Unmanned Aerial Vehicles (UAVs) localization, especially in environments with unstable or unavailable GPS signals. While neural network-based VPR methods have become mainstream, they face significant challenges on UAV platforms. Traditional CNN-based VPR models are highly sensitive to image rotation, degrading their performance in aerial-domain environments. Meanwhile, Transformer-based models have high computational complexity, making them less suitable for resource-constrained UAVs. In this letter, we propose a lightweight, rotation-invariant aerial VPR method. Our approach combines a rotation-equivariant backbone network with a rotation-invariant aggregation layer to ensure descriptor consistency across different orientations. Additionally, we propose an unsupervised training strategy that constructs higher-dimensional descriptors to optimize the model, while maintaining the lower descriptor dimensionality during application. Experimental results show that our method outperforms state-of-the-art methods across multiple aerial VPR datasets. The code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/cbbhuxx/UltraVPR</uri>.