BEVRender: Vision-Based Cross-View Vehicle Registration in Off-Road GNSS-Denied Environment

We introduce BEVRender, a novel learning-based approach for the localization of ground vehicles in Global Navigation Satellite System (GNSS)-denied off-road scenarios. These environments are typically challenging for conventional vision-based state estimation due to the lack of distinct visual landmarks and the instability of vehicle poses. To address this, BEVRender generates high-quality local bird’s-eye-view (BEV) images of the local terrain. Subsequently, these images are aligned with a georeferenced aerial map through template matching to achieve accurate cross-view registration. Our approach overcomes the inherent limitations of visual inertial odometry systems and the substantial storage requirements of image-retrieval localization strategies, which are susceptible to drift and scalability issues, respectively. Extensive experimenta- tion validates BEVRender’s advancement over existing GNSS- denied visual localization methods, demonstrating notable en- hancements in both localization accuracy and update frequency.