UltraVPR: Unsupervised Lightweight Rotation- Invariant Aerial Visual Place Recognition

Abstract

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>.