Aerial robots play a vital role in various applications where the situational awareness of the robots concerning the environment is a fundamental demand. As one such use case, drones in GPS-denied environments require equipping with different sensors (e.g., vision sensors) that provide reliable sensing results while performing pose estimation and localization. In this paper, reconstructing the maps of indoor environments alongside generating 3D scene graphs for a high-level representation using a camera mounted on a drone is targeted. Accordingly, an aerial robot equipped with a companion computer and an RGB-D camera was built and employed to be appropriately integrated with a Visual Simultaneous Localization and Mapping (VSLAM) framework proposed by the authors. To enhance the situational awareness of the robot while reconstructing maps, various structural elements, including doors and walls, were labeled with printed fiducial markers, and a dictionary of the topological relations among them was fed to the system. The VSLAM system detects markers and reconstructs the map of the indoor areas enriched with higher-level semantic entities, including corridors and rooms. Another achievement is generating multi-layered vision-based situational graphs containing enhanced hierarchical representations of the indoor environment. In this regard, integrating VSLAM into the employed drone is the primary target of this paper to provide an end-to-end robot application for GPS-denied environments. To show the practicality of the system, various real-world condition experiments have been conducted in indoor scenarios with dissimilar structural layouts. Evaluations show the proposed drone application can perform adequately w.r.t. the ground-truth data and its baseline.

翻译：空中机器人在多种应用中发挥着关键作用，其中机器人对环境的情境感知能力是基本需求。作为典型应用场景之一，在无GPS环境中工作的无人机需要配备不同传感器（如视觉传感器），以便在执行位姿估计与定位的同时提供可靠的感知结果。本文旨在利用无人机搭载的摄像头进行室内环境地图重建，并生成用于高层级表征的三维场景图。为此，本研究构建了一种配备辅助计算机和RGB-D相机的空中机器人，并将其与作者提出的视觉同步定位与地图构建（VSLAM）框架进行有效集成。为提升机器人地图重建过程中的情境感知能力，研究采用印刷基准标记标注了包括门和墙体在内的多种结构元素，并将这些元素间拓扑关系的词典输入系统。该VSLAM系统能够检测标记并重建室内区域地图，同时加入走廊、房间等高层次语义实体。另一项成果是生成了包含室内环境增强层次化表征的多层视觉情境图。在此背景下，将VSLAM集成至所采用的无人机是本文的核心目标，旨在为无GPS环境提供端到端的机器人应用。为验证系统实用性，研究者在结构布局各异的室内场景中开展了多项真实环境实验。评估结果表明，所提无人机应用在地面实况数据和基准方法方面均能展现出良好的性能。