  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 https://github.com/GongWeiSheng/DIDLM.
