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. Therefore, the dataset is named FLICAR to denote flying cars. We believe this dataset can also represent the flying car scenarios, specifically the takeoff and landing of VTOL (Vertical Takeoff and Landing) flying cars. The dataset is available for download at: https://ustc-flicar.github.io.

翻译：本文提出USTC FLICAR数据集，该数据集专为重型自主高空作业机器人的同时定位与地图构建（SLAM）及工作空间精确三维重建而开发。近年来，众多公共数据集在推动自动驾驶汽车和无人机（UAV）技术发展中发挥了重要作用。然而，这两类平台与高空作业机器人存在差异：无人机受限于有效载荷能力，而汽车则局限于二维运动。为填补这一空白，我们基于高空作业车构建了"长颈鹿"地图构建机器人，其配备多种经严格标定与时间同步的传感器：四台三维激光雷达、两台立体相机、两台单目相机、惯性测量单元（IMU）以及全球导航卫星系统/惯性导航系统（GNSS/INS）。采用激光跟踪仪记录毫米级地面真值位置。我们还制造了其地面孪生体"䶈狌"地图构建机器人以采集对比数据。所提数据集将典型的自动驾驶感知系统扩展至空中场景，因此命名为FLICAR（飞车）。我们相信该数据集还可表征飞行汽车场景，特别是垂直起降（VTOL）飞行汽车的起飞与着陆过程。数据集下载链接：https://ustc-flicar.github.io