Towards Autonomous Drone Racing without GPU Using an OAK-D Smart Camera

Controlling a bio-inspired miniature blimp using a depth sensing neural-network camera

Miniature blimps are lighter-than-air vehicles which have become an increasingly common unmanned aerial system research platform due to their extended endurance and collision tolerant design. The UNSW-C bio-inspired miniature blimp consists of a 0.5 m spherical mylar envelope filled with helium. Four fins placed along the equator provide control over the three translatory axes and yaw rotations. A gondola attached to the bottom of the blimp contains all the electronics and flight controller. Here, we focus on using the UNSW-C blimp as a platform to achieve autonomous flight in GPS-denied environments. The majority of unmanned flying systems rely on GPS or multi-camera motion capture systems for position and orientation estimation. However, such systems are expensive, difficult to set up and not compact enough to be deployed in real environments. Instead, we seek to achieve basic flight autonomy for the blimp using a low-priced and portable solution. We make use of a low-cost embedded neural network stereoscopic camera (OAK-D-PoE) for detecting and positioning the blimp while an onboard inertia measurement unit was used for orientation estimation. Flight tests and analysis of trajectories revealed that 3D position hold as well as basic waypoint navigation could be achieved with variance (<0.1 m). This performance was comparable to that when a conventional multi-camera positioning system (VICON) was used for localizing the blimp. Our results highlight the potentially favorable tradeoffs offered by such low-cost positioning systems in extending the operational domain of unmanned flight systems when direct line of sight is available.