This article presents a novel telepresence system for advancing aerial manipulation in dynamic and unstructured environments. The proposed system not only features a haptic device, but also a virtual reality (VR) interface that provides real-time 3D displays of the robot's workspace as well as a haptic guidance to its remotely located operator. To realize this, multiple sensors namely a LiDAR, cameras and IMUs are utilized. For processing of the acquired sensory data, pose estimation pipelines are devised for industrial objects of both known and unknown geometries. We further propose an active learning pipeline in order to increase the sample efficiency of a pipeline component that relies on Deep Neural Networks (DNNs) based object detection. All these algorithms jointly address various challenges encountered during the execution of perception tasks in industrial scenarios. In the experiments, exhaustive ablation studies are provided to validate the proposed pipelines. Methodologically, these results commonly suggest how an awareness of the algorithms' own failures and uncertainty (`introspection') can be used tackle the encountered problems. Moreover, outdoor experiments are conducted to evaluate the effectiveness of the overall system in enhancing aerial manipulation capabilities. In particular, with flight campaigns over days and nights, from spring to winter, and with different users and locations, we demonstrate over 70 robust executions of pick-and-place, force application and peg-in-hole tasks with the DLR cable-Suspended Aerial Manipulator (SAM). As a result, we show the viability of the proposed system in future industrial applications.

翻译：本文提出了一种新颖的遥存在系统，旨在提升动态与非结构化环境中的空中操作能力。该系统不仅配备触觉设备，还集成了虚拟现实界面，可实时显示机器人工作空间的三维场景，并为远程操作员提供触觉引导。为实现这一目标，系统采用了多种传感器，包括激光雷达、摄像头和惯性测量单元。针对获取的传感数据，我们设计了适用于已知与未知几何形状工业物体的姿态估计流程。此外，为提升依赖深度神经网络目标检测的流程组件的样本效率，我们进一步提出了一个主动学习流程。这些算法共同解决了工业场景下感知任务执行过程中遇到的各种挑战。在实验中，我们通过详尽的分析消融研究验证了所提流程的有效性。方法论上，这些结果普遍表明，对算法自身失败与不确定性的感知（即“自省”）可用于应对所遇问题。此外，我们开展了室外实验以评估整个系统在增强空中操作能力方面的有效性。特别地，通过跨越昼夜、春夏秋冬季节、不同用户与地点的飞行测试，我们采用德国航空航天中心缆索悬挂式空中操作器（SAM）展示了超过70次稳健的拾取-放置、力施加及销孔插入任务执行。最终，我们证明了所提系统在未来工业应用中的可行性。