  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.
