BEVRender：越野无GNSS环境下基于视觉的跨视角车辆配准
BEVRender: Vision-based Cross-view Vehicle Registration in Off-road GNSS-denied Environment
AI总结：
针对越野无GNSS环境下视觉定位难的问题，提出BEVRender方法，通过生成局部BEV图与地理参考航拍图模板匹配实现跨视角配准，在定位精度和更新频率上优于现有方法。
摘要：
我们提出了BEVRender，这是一种新型的基于学习的方法，用于全球导航卫星系统（GNSS）信号缺失的越野场景下地面车辆的定位。由于缺乏独特的视觉地标且车辆姿态不稳定，这类环境对传统的基于视觉的状态估计来说通常极具挑战性。为解决该问题，BEVRender会生成高质量的局部地形鸟瞰图（BEV）。随后，这些图像会通过模板匹配与带有地理参考的航拍地图对齐，以实现精准的跨视角配准。我们的方法克服了视觉惯性里程计系统的固有局限性，也解决了图像检索定位策略的大量存储需求问题——前者易出现漂移，后者则存在可扩展性问题。大量实验验证了BEVRender相较于现有无GNSS视觉定位方法的先进性，在定位精度和更新频率上均有显著提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
