GeoCluster: Enhancing Visual Place Recognition in Spatial Domain on Aerial Vehicle Platforms

Visual Place Recognition (VPR) is a critical technology for achieving robust long-term visual geo-localization. During the past few years, VPR research mainly focused on ground-based platforms in the street-level captured scenes with deep learning methods (e.g. NetVLAD, GeM), but little attention was paid to the VPR task on aerial vehicles. The algorithms and models designed for ground-based platforms are always directly applied to the aerial VPR problem. However, the viewpoint variance on Unmanned Aerial Vehicles (UAV) is much larger than the ground-based platforms. Due to the sparse distribution of aerial image features, when the viewpoint of the camera changes, the features of the query image are largely inconsistent with the descriptors in the database, which results in the failures of image retrieval and visual geo-localization. In this letter, we propose an aerial VPR enhancement module called GeoCluster , which presents a feature aggregation method using spatial clustering information to improve the robustness and consistency of the global descriptors for UAV-captured frames. Moreover, it can be applied to any NetVLAD-based VPR method and boost the pre-trained model without any further training process. By integrating GeoCluster into an existing state-of-the-art localization method, we can achieve about 10% improvement for aerial image retrieval tasks and have more accurate and robust geo-localization results. To foster future research, we make the code and datasets in this work publicly available for any researcher at https://github.com/cbbhuxx/GeoCluster.