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：摘要：近年来，毫米波扫描雷达在自动驾驶车辆定位与场景理解方面的学术兴趣和商业应用日益增长。尽管已有多个数据集支持该领域研究，但它们主要聚焦于城市或半城市环境。然而，崎岖的越野场景作为该传感器技术的重要应用领域，既带来了独特挑战，也提供了新的机遇。为此，牛津越野雷达数据集（OORD）收录了在苏格兰高地极端天气条件下的采集数据。我们向社区提供的雷达数据均附带GPS/INS参考信息，以进一步推动雷达地点识别研究。通过驾驶叉车在11次实地考察中沿四条不同路线行驶，累计完成约154公里崎岖驾驶，我们共发布超过90GiB的雷达扫描数据及GPS和IMU读数。该研究方向在文献中日益受到关注，因此我们同步发布并展示了近期开源的雷达地点识别系统及其在本数据集上的性能表现，其中包括一个已公开权重的神经网络。数据集及相关工具已免费开源发布于 https://oxford-robotics-institute.github.io/oord-dataset。