  In this work, we present a pragmatic approach to enable unmanned aerial
vehicle (UAVs) to autonomously perform highly complicated tasks of object pick
and place. This paper is largely inspired by challenge-2 of MBZIRC 2020 and is
primarily focused on the task of assembling large 3D structures in outdoors and
GPS-denied environments. Primary contributions of this system are: (i) a novel
computationally efficient deep learning based unified multi-task visual
perception system for target localization, part segmentation, and tracking,
(ii) a novel deep learning based grasp state estimation, (iii) a retracting
electromagnetic gripper design, (iv) a remote computing approach which exploits
state-of-the-art MIMO based high speed (5000Mb/s) wireless links to allow the
UAVs to execute compute intensive tasks on remote high end compute servers, and
(v) system integration in which several system components are weaved together
in order to develop an optimized software stack. We use DJI Matrice-600 Pro, a
hex-rotor UAV and interface it with the custom designed gripper. Our framework
is deployed on the specified UAV in order to report the performance analysis of
the individual modules. Apart from the manipulation system, we also highlight
several hidden challenges associated with the UAVs in this context.
