GPS拒止环境下生成重建三维场景图的无人机辅助视觉SLAM
UAV-assisted Visual SLAM Generating Reconstructed 3D Scene Graphs in GPS-denied Environments
AI总结：
针对GPS拒止环境下无人机的态势感知需求，提出集成VSLAM框架的无人机系统，通过标记检测重建含语义实体的室内地图并生成多层场景图，经真实场景实验验证性能良好。
摘要：
空中机器人在各类应用中发挥着关键作用，而机器人对环境的态势感知是这类应用的核心需求。作为其中一个用例，处于GPS拒止环境中的无人机需要搭载视觉传感器等不同类型的传感器，在执行位姿估计与定位时提供可靠的感知结果。本文旨在利用搭载在无人机上的摄像头，重建室内环境地图，同时生成用于高层表示的三维场景图。为此，本文构建了一台搭载配套计算机与RGB-D摄像头的空中机器人，并将其与作者提出的视觉同时定位与地图构建（Visual Simultaneous Localization and Mapping, VSLAM）框架妥善集成。为在重建地图的过程中提升机器人的态势感知能力，研究人员为门、墙体等各类结构元素贴上了打印的基准标记，并向系统输入了这些元素之间的拓扑关系字典。该VSLAM系统可检测标记，并重建富含走廊、房间等更高级语义实体的室内区域地图。另一项成果是生成了多层级的基于视觉的态势图，其中包含增强的室内环境分层表示。就此而言，将VSLAM集成到所使用的无人机中是本文的主要目标，旨在为GPS拒止环境提供一套端到端的机器人应用方案。为验证该系统的实用性，研究人员在结构布局各异的室内场景中开展了多种真实环境条件下的实验。评估结果表明，相较于真值数据与基线方法，本文提出的无人机应用可实现良好的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
