6 accepted papers
In this work, we propose HE-VPR, a visual place recognition (VPR) framework that incorporates height estimation. Our system decouples height inference from place recognition, allowing both modules to share a frozen DINOv2 backbone. Two lightweight bypass adapter branches are integrated into our syst…
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-b…
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-b
Visual localization plays an essential role in the autonomous flight of Unmanned Aerial Vehicles (UAVs) especially for the Global Navigation Satellite System (GNSS) denied environments. Existing aerial-based visual localization methods mainly focus on eliminating image variance between database map
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 wa
In this work, we propose a solution that leverages geospatial data to initialize the monocular visual-inertial navigation system. For Visual-Inertial Navigation Systems (VINS) operating on UAVs, the ability to perform initialization and relocalization in mid-air is essential. However, degenerate mot