RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization
For researchers in visual localization, this work provides a robust solution and benchmark for real-world conditions like weather and sensor noise.
The paper tackles metric cross-view geo-localization using panoramic ground images and OpenStreetMap, introducing a large-scale benchmark (CV-RHO) and a model (RHO) that achieves up to 20% performance gain over state-of-the-art baselines.
Metric Cross-View Geo-Localization (MCVGL) aims to estimate the 3-DoF camera pose (position and heading) by matching ground and satellite images. In this work, instead of pinhole and satellite images, we study robust MCVGL using holistic panoramas and OpenStreetMap (OSM). To this end, we establish a large-scale MCVGL benchmark dataset, CV-RHO, with over 2.7M images under different weather and lighting conditions, as well as sensor noise. Furthermore, we propose a model termed RHO with a two-branch Pin-Pan architecture for accurate visual localization. A Split-Undistort-Merge (SUM) module is introduced to address the panoramic distortion, and a Position-Orientation Fusion (POF) mechanism is designed to enhance the localization accuracy. Extensive experiments prove the value of our CV-RHO dataset and the effectiveness of the RHO model, with a significant performance gain up to 20% compared with the state-of-the-art baselines. Project page: https://github.com/InSAI-Lab/RHO.