We present a novel framework using Energy-Based Models (EBMs) for localizing
a ground vehicle mounted with a range sensor against satellite imagery in the
absence of GPS. Lidar sensors have become ubiquitous on autonomous vehicles for
describing its surrounding environment. Map priors are typically built using
the same sensor modality for localization purposes. However, these map building
endeavors using range sensors are often expensive and time-consuming.
Alternatively, we leverage the use of satellite images as map priors, which are
widely available, easily accessible, and provide comprehensive coverage. We
propose a method using convolutional transformers that performs accurate
metric-level localization in a cross-modal manner, which is challenging due to
the drastic difference in appearance between the sparse range sensor readings
and the rich satellite imagery. We train our model end-to-end and demonstrate
our approach achieving higher accuracy than the state-of-the-art on KITTI,
Pandaset, and a custom dataset.

使用基于能量的模型框架对非 GPS 地面车辆进行定位，并结合激光雷达传感器和卫星图像进行跨模态测量级定位的方法，实验结果表明其在 KITTI、Pandaset 和自定义数据集上的表现优于现有最优解的方法。