This study presents a comprehensive multi-sensor dataset designed for 3D
mapping in challenging indoor and outdoor environments. The dataset comprises
data from infrared cameras, depth cameras, LiDAR, and 4D millimeter-wave radar,
facilitating exploration of advanced perception and mapping techniques.
Integration of diverse sensor data enhances perceptual capabilities in extreme
conditions such as rain, snow, and uneven road surfaces. The dataset also
includes interactive robot data at different speeds indoors and outdoors,
providing a realistic background environment. Slam comparisons between similar
routes are conducted, analyzing the influence of different complex scenes on
various sensors. Various SLAM algorithms are employed to process the dataset,
revealing performance differences among algorithms in different scenarios. In
summary, this dataset addresses the problem of data scarcity in special
environments, fostering the development of perception and mapping algorithms
for extreme conditions. Leveraging multi-sensor data including infrared, depth
cameras, LiDAR, 4D millimeter-wave radar, and robot interactions, the dataset
advances intelligent mapping and perception capabilities.Our dataset is
available at this https URL

该研究提供了一个综合多传感器数据集，旨在挑战性室内外环境中进行 3D 地图制作。数据集包括红外相机、深度相机、LiDAR 和 4D 毫米波雷达的数据，提供了探索先进感知和制图技术的可能性。多种传感器数据的整合增强了在极端条件下（如雨、雪和不平整的道路表面）的感知能力。数据集还包括室内外以不同速度运行的交互式机器人数据，提供了逼真的背景环境。通过类似路线的 SLAM 比较，分析了不同复杂场景对各个传感器的影响。采用各种 SLAM 算法处理数据集，揭示了不同场景中算法性能的差异。总之，该数据集解决了特殊环境中数据稀缺的问题，促进了在极端条件下感知和制图算法的发展。利用包括红外、深度相机、LiDAR、4D 毫米波雷达和机器人交互的多传感器数据，该数据集推动了智能制图和感知能力的进步。我们的数据集可在此链接获取：https://example.com/dataset

DIDLM：具有红外摄像头、深度摄像头、LiDAR 和 4D 毫米波雷达的全面多传感器数据集，用于 3D 地图在具有挑战性的场景中

DIDLM:A Comprehensive Multi-Sensor Dataset with Infrared Cameras, Depth  Cameras, LiDAR, and 4D Millimeter-Wave Radar in Challenging Scenarios for 3D  Mapping