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“BEVRender: Vision-based Cross-view Vehicle Registration in Off-road GNSS-denied Environment” by L. Jin, W. Dong, W. Wang, and M. Kaess. In Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS, (Abu Dhabi, UAE), Oct. 2024.
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 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 entry:
@inproceedings{Jin24iros,
author = {L. Jin and W. Dong and W. Wang and M. Kaess},
title = {{BEVRender}: Vision-based Cross-view Vehicle Registration in
Off-road {GNSS}-denied Environment},
booktitle = {Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and
Systems, IROS},
address = {Abu Dhabi, UAE},
month = oct,
year = {2024}
}