BEVRender: Vision-based Cross-view Vehicle Registration in Off-road GNSS-denied Environment
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910736757817344 |
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| author | Jin, Lihong Dong, Wei Wang, Wenshan Kaess, Michael |
| author_facet | Jin, Lihong Dong, Wei Wang, Wenshan Kaess, Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09001 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BEVRender: Vision-based Cross-view Vehicle Registration in Off-road GNSS-denied Environment Jin, Lihong Dong, Wei Wang, Wenshan Kaess, Michael Robotics I.2.9 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. |
| title | BEVRender: Vision-based Cross-view Vehicle Registration in Off-road GNSS-denied Environment |
| topic | Robotics I.2.9 |
| url | https://arxiv.org/abs/2405.09001 |