用地面雷达与卫星图像匹配，实现无GPS全局定位。
RaSCL: Radar to Satellite Crossview Localization
- 融合地面雷达与卫星影像，通过联合优化相对与绝对位姿
- 在城市、郊区及水面船只上实现高精度定位，无需初始GPS
- 适合复杂环境下的无人车、无人机等自主系统
GNSS在许多实时自主应用场景中不可靠、不精确且不足。本文提出一种无GNSS的全局定位方案，通过将地面成像雷达与高空RGB影像进行配准，并联合优化里程计提供的相对位姿与高空注册获得的全局位姿。以往工作使用地面传感器与高空影像的多种组合，以及手工设计和基于深度学习的特征提取与匹配方法。本研究揭示了仅利用地面雷达与单个地理参考初始猜测，从高空RGB图像中提取关键特征以实现有效全局定位的可行性。我们通过在不同地理条件和机器人平台上的数据集验证了该方法，包括无人水面船（USV）以及城市与郊区驾驶数据集。
原文摘要 · Abstract (English)
GNSS is unreliable, inaccurate, and insufficient in many real-time autonomous field applications. In this work, we present a GNSS-free global localization solution that contains a method of registering imaging radar on the ground with overhead RGB imagery, with joint optimization of relative poses from odometry and global poses from our overhead registration. Previous works have used various combinations of ground sensors and overhead imagery, and different feature extraction and matching methods. These include various handcrafted and deep-learning-based methods for extracting features from overhead imagery. Our work presents insights on extracting essential features from RGB overhead images for effective global localization against overhead imagery using only ground radar and a single georeferenced initial guess. We motivate our method by evaluating it on datasets in diverse geographic conditions and robotic platforms, including on an Unmanned Surface Vessel (USV) as well as urban and suburban driving datasets.
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