NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and
  Mapping
Jun Zhang Huayang Zhuge Yiyao Liu Guohao Peng Zhenyu Wu Haoyuan Zhang Qiyang Lyu Heshan Li Chunyang Zhao Dogan Kircali Sanat Mharolkar Xun Yang Su Yi Yuanzhe Wang Danwei Wang
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