Road Structure Inspired UGV-Satellite Cross-View Geo-Localization

Abstract

This article presents a new approach to address the challenge of combining ground-based LiDAR data with satellite images for cross-view image geo-localization. The task is to figure out the position and orientation of the LiDAR within the given satellite image. While previous research has mainly focused on imagery, the integration of ground-based point clouds with satellite images has been limited due to significant differences in modalities. To release this limitation, we propose a novel method that utilizes the road structure as a consistent reference between satellite images and ground LiDAR data for accurate geo-localization. Our methodology encompasses the extraction of road structures from both point clouds and satellite images. To extract road structures from point clouds, we leverage the enhanced viewpoint beam model, which effectively captures the spatial characteristics of ground landmarks. In addition, we utilize fractional-order differential-based super-resolution technology for satellite images to improve road structure detection, ensuring reliable performance across different altitudes. Following this, our approach involves matching road structures from the ground and satellite views, simplifying the localization process to a template-matching task. Consequently, we successfully address the challenge of accurately determining the 3-DoF pose of the LiDAR within the satellite image context. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in geo-localization, outperforming comparable methods. In addition, the approach shows versatility across various altitudes.

Keywords

• Cross-modality alignment
• cross-view pose estimation
• fractional-order differentiation
• road structure extraction
• super-resolution