Visual Place Recognition for Large-Scale UAV Applications

TL;DR

This paper introduces LASED, a large-scale aerial dataset for UAV visual place recognition, and proposes steerable CNNs to improve robustness against rotational variance, significantly advancing UAV localization capabilities.

Contribution

The paper presents LASED, a comprehensive large-scale aerial dataset, and integrates steerable CNNs to explicitly handle rotational ambiguity in UAV visual place recognition.

Findings

Models trained on LASED outperform those trained on smaller datasets.

Steerable CNNs achieve 12% higher recall than non-steerable networks.

The combined approach enhances robustness and generalization in aerial vPR.

Abstract

Visual Place Recognition (vPR) plays a crucial role in Unmanned Aerial Vehicle (UAV) navigation, enabling robust localization across diverse environments. Despite significant advancements, aerial vPR faces unique challenges due to the limited availability of large-scale, high-altitude datasets, which limits model generalization, along with the inherent rotational ambiguity in UAV imagery. To address these challenges, we introduce LASED, a large-scale aerial dataset with approximately one million images, systematically sampled from 170,000 unique locations throughout Estonia over a decade, offering extensive geographic and temporal diversity. Its structured design ensures clear place separation significantly enhancing model training for aerial scenarios. Furthermore, we propose the integration of steerable Convolutional Neural Networks (CNNs) to explicitly handle rotational variance, leveraging their inherent rotational equivariance to produce robust, orientation-invariant feature representations. Our extensive benchmarking demonstrates that models trained on LASED achieve significantly higher recall compared to those trained on smaller, less diverse datasets, highlighting the benefits of extensive geographic coverage and temporal diversity. Moreover, steerable CNNs effectively address rotational ambiguity inherent in aerial imagery, consistently outperforming conventional convolutional architectures, achieving on average 12\% recall improvement over the best-performing non-steerable network. By combining structured, large-scale datasets with rotation-equivariant neural networks, our approach significantly enhances model robustness and generalization for aerial vPR.