Abstract
Recent advances have shown for the first time that it is possible to beat a human with an autonomous drone in a drone race. However, this solution relies heavily on external sensors, specifically on the use of a motion capture system. Thus, a truly autonomous solution demands performing computationally intensive tasks such as gate detection, drone localisation, and state estimation. To this end, other solutions rely on specialised hardware such as graphics processing units (GPUs) whose onboard hardware versions are not as powerful as those available for desktop and server computers. An alternative is to combine specialised hardware with smart sensors capable of processing specific tasks on the chip, alleviating the need for the onboard processor to perform these computations. Motivated by this, we present the initial results of adapting a novel smart camera, known as the OpenCV AI Kit or OAK-D, as part of a solution for the ADR running entirely on board. This smart camera performs neural inference on the chip that does not use a GPU. It can also perform depth estimation with a stereo rig and run neural network models using images from a 4K colour camera as the input. Additionally, seeking to limit the payload to 200 g, we present a new 3D-printed design of the camera’s back case, reducing the original weight 40%, thus enabling the drone to carry it in tandem with a host onboard computer, the Intel Stick compute, where we run a controller based on gate detection. The latter is performed with a neural model running on an OAK-D at an operation frequency of 40 Hz, enabling the drone to fly at a speed of 2 m/s. We deem these initial results promising toward the development of a truly autonomous solution that will run intensive computational tasks fully on board.
Highlights
Since its creation in IROS 2016, the Autonomous Drone Racing (ADR) competition has posed the challenge of developing an autonomous drone capable of beating a human in a drone race
We were able to run nodes on the drone’s onboard computer, an external onboard computer that is carried by the drone, whose purpose is serving as host to the OAK-D smart camera, the flight controller, and to publish low-resolution colour images from OAK-D
We presented the initial results of adapting a novel smart camera to become part of a solution for Autonomous Drone Racing
Summary
Since its creation in IROS 2016, the Autonomous Drone Racing (ADR) competition has posed the challenge of developing an autonomous drone capable of beating a human in a drone race. The first editions of this competition gathered research groups whose first solutions broke down the problem into three main problems to be addressed: (1) gate detection; (2) drone localisation on the race track for control and navigation; (3) suitable hardware for onboard processing. Visual simultaneous localisation and mapping (SLAM) and visual odometry techniques were employed, seeking to provide global localisation on the race track, which was exploited by teams to implement a waypoint-based navigation system [2]. Further improvements proposed adding local pose estimation with respect to the gate in a top-down navigation scheme where the controller drives the drone to follow a global trajectory, which is refined once the drone flies toward the gate [4,5]. The Game of Drones competition at NeurIPS [6] called upon researchers to ignore hardware and efficient performance to focus on high-level navigation strategies while seeking to push for
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