  In this paper, we present the USTC FLICAR Dataset, which is dedicated to the
development of simultaneous localization and mapping and precise 3D
reconstruction of the workspace for heavy-duty autonomous aerial work robots.
In recent years, numerous public datasets have played significant roles in the
advancement of autonomous cars and unmanned aerial vehicles (UAVs). However,
these two platforms differ from aerial work robots: UAVs are limited in their
payload capacity, while cars are restricted to two-dimensional movements. To
fill this gap, we create the "Giraffe" mapping robot based on a bucket truck,
which is equipped with a variety of well-calibrated and synchronized sensors:
four 3D LiDARs, two stereo cameras, two monocular cameras, Inertial Measurement
Units (IMUs), and a GNSS/INS system. A laser tracker is used to record the
millimeter-level ground truth positions. We also make its ground twin, the
"Okapi" mapping robot, to gather data for comparison. The proposed dataset
extends the typical autonomous driving sensing suite to aerial scenes,
demonstrating the potential of combining autonomous driving perception systems
with bucket trucks to create a versatile autonomous aerial working platform.
Moreover, based on the Segment Anything Model (SAM), we produce the Semantic
FLICAR dataset, which provides fine-grained semantic segmentation annotations
for multimodal continuous data in both temporal and spatial dimensions. The
dataset is available for download at: https://ustc-flicar.github.io/.
