Simultaneous Localization and Mapping (SLAM) is moving towards a robust perception age. However, LiDAR- and visual- SLAM may easily fail in adverse conditions (rain, snow, smoke and fog, etc.). In comparison, SLAM based on 4D Radar, thermal camera and IMU can work robustly. But only a few literature can be found. A major reason is the lack of related datasets, which seriously hinders the research. Even though some datasets are proposed based on 4D radar in past four years, they are mainly designed for object detection, rather than SLAM. Furthermore, they normally do not include thermal camera. Therefore, in this paper, NTU4DRadLM is presented to meet this requirement. The main characteristics are: 1) It is the only dataset that simultaneously includes all 6 sensors: 4D radar, thermal camera, IMU, 3D LiDAR, visual camera and RTK GPS. 2) Specifically designed for SLAM tasks, which provides fine-tuned ground truth odometry and intentionally formulated loop closures. 3) Considered both low-speed robot platform and fast-speed unmanned vehicle platform. 4) Covered structured, unstructured and semi-structured environments. 5) Considered both middle- and large- scale outdoor environments, i.e., the 6 trajectories range from 246m to 6.95km. 6) Comprehensively evaluated three types of SLAM algorithms. Totally, the dataset is around 17.6km, 85mins, 50GB and it will be accessible from this link: https://github.com/junzhang2016/NTU4DRadLM

翻译：同时定位与地图构建（SLAM）正迈向鲁棒感知时代。然而，基于激光雷达和视觉的SLAM在恶劣环境（雨、雪、烟雾、雾等）下容易失效。相比之下，基于4D雷达、热成像相机和惯性测量单元（IMU）的SLAM能够鲁棒工作，但相关文献较少。主要原因是缺乏相关数据集，这严重阻碍了研究进展。尽管过去四年已有一些基于4D雷达的数据集被提出，但它们主要针对目标检测任务而非SLAM，且通常不包含热成像相机。因此，本文提出NTU4DRadLM以满足这一需求。其主要特征包括：1）它是唯一同时包含全部6类传感器（4D雷达、热成像相机、IMU、3D激光雷达、视觉相机和RTK GPS）的数据集；2）专门针对SLAM任务设计，提供了微调的真值里程计和精心构造的闭环场景；3）同时考虑了低速机器人平台和高速无人车平台；4）覆盖结构化、非结构化和半结构化环境；5）涵盖了中尺度和大尺度室外环境，即6条轨迹长度从246米到6.95公里不等；6）综合评价了三种类型SLAM算法。该数据集总长约17.6公里，时长85分钟，体积50GB，可通过此链接访问：https://github.com/junzhang2016/NTU4DRadLM