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

Lihong Jin; Wei Dong; Wenshan Wang; Michael Kaess

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arXiv · 2405.09001

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

Lihong Jin·Wei Dong·Wenshan Wang·Michael Kaess

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Abstract

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 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 experimentation validates BEVRender's advancement over existing GNSS-denied visual localization methods, demonstrating notable enhancements in both localization accuracy and update frequency.

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BibTeX RIS

Lihong Jin, Wei Dong, Wenshan Wang, Michael Kaess. 2024-12-10. BEVRender: Vision-based Cross-view Vehicle Registration in Off-road GNSS-denied Environment. https://arxiv.org/abs/2405.09001

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