  Simultaneous Localization and Mapping (SLAM) technology has been widely
applied in various robotic scenarios, from rescue operations to autonomous
driving. However, the generalization of SLAM algorithms remains a significant
challenge, as current datasets often lack scalability in terms of platforms and
environments. To address this limitation, we present FusionPortableV2, a
multi-sensor SLAM dataset featuring notable sensor diversity, varied motion
patterns, and a wide range of environmental scenarios. Our dataset comprises
$27$ sequences, spanning over $2.5$ hours and collected from four distinct
platforms: a handheld suite, wheeled and legged robots, and vehicles. These
sequences cover diverse settings, including buildings, campuses, and urban
areas, with a total length of $38.7km$. Additionally, the dataset includes
ground-truth (GT) trajectories and RGB point cloud maps covering approximately
$0.3km^2$. To validate the utility of our dataset in advancing SLAM research,
we assess several state-of-the-art (SOTA) SLAM algorithms. Furthermore, we
demonstrate the dataset's broad applicability beyond traditional SLAM tasks by
investigating its potential for monocular depth estimation. The complete
dataset, including sensor data, GT, and calibration details, is accessible at
https://fusionportable.github.io/dataset/fusionportable_v2.
