BEVRender: Vision-based Cross-view Vehicle Registration in Off-road GNSS-denied Environment
| Autor: | Jin, Lihong, Dong, Wei, Kaess, Michael |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 8 pages, 6 figures |
| Databáze: | arXiv |
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