Compared to the onboard camera and laser scanner, radar sensor provides lighting and weather invariant sensing, which is naturally suitable for long-term localization under adverse conditions. However, radar data is sparse and noisy, resulting in challenges for radar mapping. On the other hand, the most popular available map currently is built by lidar. In this paper, we propose an end-to-end deep learning framework for Radar Localization on Lidar Map (RaLL) to bridge the gap, which not only achieves the robust radar localization but also exploits the mature lidar mapping technique, thus reducing the cost of radar mapping. We first embed both sensor modals into a common feature space by a neural network. Then multiple offsets are added to the map modal for exhaustive similarity evaluation against the current radar modal, yielding the regression of the current pose. Finally, we apply this differentiable measurement model to a Kalman Filter (KF) to learn the whole sequential localization process in an end-to-end manner. \textit{The whole learning system is differentiable with the network based measurement model at the front-end and KF at the back-end.} To validate the feasibility and effectiveness, we employ multi-session multi-scene datasets collected from the real world, and the results demonstrate that our proposed system achieves superior performance over $90km$ driving, even in generalization scenarios where the model training is in UK, while testing in South Korea. We also release the source code publicly.

翻译：与机上相机和激光扫描仪相比,雷达传感器提供照明和天气变异感测,这自然适合在不利条件下长期定位,然而,雷达数据稀少而吵闹,导致雷达制图方面的挑战。另一方面,目前最受欢迎的地图是由Lidar建造的。在本文中,我们提议在Lidar地图(RALL)上为雷达定位提供端到端深学习框架,以弥合差距,这不仅实现强力雷达本地化,而且利用成熟的Lidar绘图技术,从而减少雷达制图费用。我们首先将两个传感器模型通过神经网络嵌入一个共同的地貌空间。然后在地图模型中添加多项抵消,对当前雷达模型进行详尽的类似性评估,从而导致当前模型的回归。最后,我们将这一不同的测量模型应用到Kalman过滤器(KF),以端到端到端,同时利用成熟的Lidaldar 绘图技术来学习整个连续本地化进程。\ Textitit{整个学习系统与基于网络的测量模型模型通过一个神经网络的神经空间空间空间空间空间空间空间空间空间空间空间空间测试模型,然后测试, 将我们所收集到的高级系统, 和多端数据。