USTC FLICAR: A Sensors Fusion Dataset of LiDAR-Inertial-Camera for Heavy-duty Autonomous Aerial Work Robots
This dataset fills a gap for researchers developing SLAM and 3D reconstruction systems for aerial work robots, which differ from UAVs and cars, but it is incremental as it extends existing sensing suites to a new domain.
The authors tackled the lack of public datasets for heavy-duty autonomous aerial work robots by creating the USTC FLICAR Dataset, which includes sensor fusion data from LiDAR, cameras, and IMUs on a bucket truck platform, with millimeter-level ground truth positions and semantic segmentation annotations.
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/.