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/.

翻译：本文提出USTC FLICAR数据集，专为重型自主高空作业机器人的同步定位与建图及工作空间精确三维重建而开发。近年来，众多公开数据集在自主驾驶汽车与无人机（UAV）的发展中发挥了重要作用。然而，这两种平台与高空作业机器人存在本质差异：无人机有效载荷能力有限，而汽车仅局限于二维运动。为填补这一空白，我们基于高空作业车构建了"Giraffe"建图机器人，其搭载多种经过精确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）及GNSS/INS系统。采用激光跟踪仪记录毫米级真值位置。同时制造其地面孪生版本"Okapi"建图机器人以采集对比数据。本数据集将典型自动驾驶感知套件扩展至高空场景，展示了将自动驾驶感知系统与高空作业车结合打造通用化自主高空作业平台的潜力。此外，基于Segment Anything Model（SAM）模型，我们生成了Semantic FLICAR数据集，为时空维度的多模态连续数据提供细粒度语义分割标注。数据集可通过以下链接获取：https://ustc-flicar.github.io/。