There is a growing academic interest as well as commercial exploitation of millimetre-wave scanning radar for autonomous vehicle localisation and scene understanding. Although several datasets to support this research area have been released, they are primarily focused on urban or semi-urban environments. Nevertheless, rugged offroad deployments are important application areas which also present unique challenges and opportunities for this sensor technology. Therefore, the Oxford Offroad Radar Dataset (OORD) presents data collected in the rugged Scottish highlands in extreme weather. The radar data we offer to the community are accompanied by GPS/INS reference - to further stimulate research in radar place recognition. In total we release over 90GiB of radar scans as well as GPS and IMU readings by driving a diverse set of four routes over 11 forays, totalling approximately 154km of rugged driving. This is an area increasingly explored in literature, and we therefore present and release examples of recent open-sourced radar place recognition systems and their performance on our dataset. This includes a learned neural network, the weights of which we also release. The data and tools are made freely available to the community at https://oxford-robotics-institute.github.io/oord-dataset.

翻译：学术界对毫米波扫描雷达在自动驾驶车辆定位与场景理解中的应用日益关注，商业开发也逐步深入。尽管已有多个支持该研究领域的数据集发布，但它们主要集中于城市或半城市环境。然而，崎岖越野环境是重要的应用场景，也为该传感技术带来了独特的挑战与机遇。为此，牛津越野雷达数据集（OORD）提供了在苏格兰高地恶劣天气条件下采集的数据。我们向研究社区提供的雷达数据均配有GPS/INS参考信息，以进一步推动雷达位置识别领域的研究。通过沿4条不同路线进行11次勘测，我们总计发布了超过90GiB的雷达扫描数据以及GPS和IMU读数，累计约154公里的崎岖地形行驶里程。该领域在文献中日益受到关注，因此我们展示并发布了近期开源雷达位置识别系统的示例及其在本数据集上的性能评估，其中包括一个已训练的神经网络模型，其权重参数亦同步公开。所有数据与工具均通过https://oxford-robotics-institute.github.io/oord-dataset 向研究社区免费开放。