Radar sensor provides lighting and weather invariant sensing, which is naturally suitable for long-term localization in outdoor scenes. On the other hand, the most popular available map currently is built by lidar. In this paper, we propose a deep neural network for end-to-end learning of radar localization on lidar map to bridge the gap. 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 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 to learn the whole sequential localization process in an end-to-end manner. 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.

中文翻译：

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RaLL：使用可微测量模型在激光雷达地图上进行端到端雷达定位

雷达传感器提供光照和天气不变的感知，自然适合户外场景的长期定位。另一方面，目前最流行的可用地图是由激光雷达构建的。在本文中，我们提出了一种深度神经网络，用于在激光雷达地图上进行雷达定位的端到端学习，以弥补这一差距。我们首先通过神经网络将两种传感器模态嵌入到一个公共特征空间中。然后将多个偏移量添加到地图模态，以针对当前雷达模态进行相似性评估，从而产生当前位姿的回归。最后，我们将此可微测量模型应用于卡尔曼滤波器，以端到端的方式学习整个顺序定位过程。为了验证可行性和有效性，我们采用了从现实世界中收集的多会话多场景数据集，结果表明，我们提出的系统在超过 90 公里的驾驶中实现了卓越的性能，即使在模型训练在英国进行的泛化场景中，在韩国进行测试也是如此。我们还公开发布源代码。