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: this https URL

在这篇论文中，我们介绍了一个新的数据集，NTU4DRadLM，它同时包含了 6 种传感器：4D 雷达、热像仪、IMU、3D 激光雷达、视觉相机和实时定位 RTK GPS，具体设计用于 SLAM 任务，并提供了经过精调的真实轨迹和故意设计的闭环，以综合评估三种类型的 SLAM 算法。

NTU4DRadLM：4D 雷达中心化多模态数据集用于定位和建图

NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and  Mapping

We present the first learning-based visual odometry (VO) model, which
generalizes to multiple datasets and real-world scenarios and outperforms
geometry-based methods in challenging scenes. We achieve this by leveraging the
SLAM dataset TartanAir, which provides a large amount of diverse synthetic data
in challenging environments. Furthermore, to make our VO model generalize
across datasets, we propose an up-to-scale loss function and incorporate the
camera intrinsic parameters into the model. Experiments show that a single
model, TartanVO, trained only on synthetic data, without any finetuning, can be
generalized to real-world datasets such as KITTI and EuRoC, demonstrating
significant advantages over the geometry-based methods on challenging
trajectories. Our code is available at this https URL

本研究提出了首个基于学习的视觉里程计模型，利用 TartanAir SLAM 数据集中提供的大量多样化的合成数据，采用尺度损失函数和相机内参将模型泛化到多个数据集和真实世界场景中，无需微调即可应用于 KITTI 和 EuRoC 数据集，并在具有挑战性的轨迹上显著优于基于几何的方法。