  4D radars are increasingly favored for odometry and mapping of autonomous
systems due to their robustness in harsh weather and dynamic environments.
Existing datasets, however, often cover limited areas and are typically
captured using a single platform. To address this gap, we present a diverse
large-scale dataset specifically designed for 4D radar-based localization and
mapping. This dataset was gathered using three different platforms: a handheld
device, an e-bike, and an SUV, under a variety of environmental conditions,
including clear days, nighttime, and heavy rain. The data collection occurred
from September 2023 to February 2024, encompassing diverse settings such as
roads in a vegetated campus and tunnels on highways. Each route was traversed
multiple times to facilitate place recognition evaluations. The sensor suite
included a 3D lidar, 4D radars, stereo cameras, consumer-grade IMUs, and a
GNSS/INS system. Sensor data packets were synchronized to GNSS time using a
two-step process: a convex hull algorithm was applied to smooth host time
jitter, and then odometry and correlation algorithms were used to correct
constant time offsets. Extrinsic calibration between sensors was achieved
through manual measurements and subsequent nonlinear optimization. The
reference motion for the platforms was generated by registering lidar scans to
a terrestrial laser scanner (TLS) point cloud map using a lidar inertial
odometry (LIO) method in localization mode. Additionally, a data reversion
technique was introduced to enable backward LIO processing. We believe this
dataset will boost research in radar-based point cloud registration, odometry,
mapping, and place recognition.
