Paper ID: 2405.09001

BEVRender: Vision-based Cross-view Vehicle Registration in Off-road GNSS-denied Environment

Lihong Jin, Wei Dong, Michael Kaess

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 geo-referenced aerial map via 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 experimentation validates BEVRender's advancement over existing GNSS-denied visual localization methods, demonstrating notable enhancements in both localization accuracy and update frequency. The code for BEVRender will be made available soon.

Submitted: May 14, 2024