Towards High-Speed Localisation for Autonomous Drone Racing

Cocoma-Ortega, J. Arturo; Martínez-Carranza, José

doi:10.1007/978-3-030-33749-0_59

Cited by 16 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of deep learning has revolutionised the development of autonomous vehicles due to their robustness and versatility in providing solutions for autonomous drone racing, for example, gating detection, relative positioning [19][20][21][22][23], flight commands [8,24,25], actions [2], speed, and even the direction of the drone [4,19]. In addition, deep learning makes it possible to transfer the knowledge acquired in simulation environments to the real world [21,26].…”

Section: Related Workmentioning

confidence: 99%

“…Hence, one of the strategies involves dividing the tasks into several modules and executing them in parallel so that the drones can provide information through the cameras and, at the same time, detect risky situations for the drones. Additionally, deep learning has been used to detect multiple objects of interest, improve navigation [45] and obtain 6DOF positioning [46] or relative positioning [5,23]. Even reinforcement learning has been used to enhance flight precision [39,47].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Rojas-Perez

Martínez-Carranza

2021

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Rojas-Perez

Martínez-Carranza

2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The estimates are used by a predictive controller to follow a flight path planned upon the estimated gate position. In a similar fashion, in [27,28], the authors have adapted PoseNet [6], a CNN network used for camera relocalization, to calculate the position of the drone regarding to a gate. The output of the network provides a vector with the values of x, y and z in meters, which then are used by a PID controller to command the drone, first to center itself regarding to the center of the gate, and once centered, to cross it.…”

Section: Related Workmentioning

confidence: 99%

DeepPilot: A CNN for Autonomous Drone Racing

Rojas-Perez

Martínez-Carranza

2020

Sensors

Self Cite

View full text Add to dashboard Cite

Autonomous Drone Racing (ADR) was first proposed in IROS 2016. It called for the development of an autonomous drone capable of beating a human in a drone race. After almost five years, several teams have proposed different solutions with a common pipeline: gate detection; drone localization; and stable flight control. Recently, Deep Learning (DL) has been used for gate detection and localization of the drone regarding the gate. However, recent competitions such as the Game of Drones, held at NeurIPS 2019, called for solutions where DL played a more significant role. Motivated by the latter, in this work, we propose a CNN approach called DeepPilot that takes camera images as input and predicts flight commands as output. These flight commands represent: the angular position of the drone’s body frame in the roll and pitch angles, thus producing translation motion in those angles; rotational speed in the yaw angle; and vertical speed referred as altitude h. Values for these 4 flight commands, predicted by DeepPilot, are passed to the drone’s inner controller, thus enabling the drone to navigate autonomously through the gates in the racetrack. For this, we assume that the next gate becomes visible immediately after the current gate has been crossed. We present evaluations in simulated racetrack environments where DeepPilot is run several times successfully to prove repeatability. In average, DeepPilot runs at 25 frames per second (fps). We also present a thorough evaluation of what we called a temporal approach, which consists of creating a mosaic image, with consecutive camera frames, that is passed as input to the DeepPilot. We argue that this helps to learn the drone’s motion trend regarding the gate, thus acting as a local memory that leverages the prediction of the flight commands. Our results indicate that this purely DL-based artificial pilot is feasible to be used for the ADR challenge.

show abstract

“…Several applications of (semi-)autonomous robots requiring ultra-low latency (<10 ms) are fast-emerging. Primary examples include Tactile Internet [1] and autonomous drone racing [2]. Violation of latency requirements pose catastrophic consequences, such as crashes.…”

Section: Introductionmentioning

confidence: 99%

FEEL: Fast, Energy-Efficient Localization for Autonomous Indoor Vehicles

Gokhale¹,

Barrera²,

Prasad³

2021

Preprint

View full text Add to dashboard Cite

Autonomous vehicles have created a sensation in both outdoor and indoor applications. The famous indoor usecase is process automation inside a warehouse using Autonomous Indoor Vehicles (AIV). These vehicles need to locate themselves not only with an accuracy of a few centimeters but also within a few milliseconds in an energy-efficient manner. Due to these challenges, localization is a holy grail. In this paper, we propose FEEL -an indoor localization system that uses a fusion of three low-energy sensors: IMU, UWB, and radar. We provide detailed software and hardware architecture of FEEL. Further, we propose Adaptive Sensing Algorithm (ASA) for opportunistically minimizing energy consumption of FEEL by adjusting the sensing frequency to the dynamics of the physical environment. Our extensive performance evaluation over diverse test settings reveal that FEEL provides a localization accuracy of <7 cm with ultra-low latency of ≈3 ms. Further, ASA yields up to 20% energy saving with only a marginal trade off in accuracy.

show abstract

Towards High-Speed Localisation for Autonomous Drone Racing

Cited by 16 publications

References 20 publications

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

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

DeepPilot: A CNN for Autonomous Drone Racing

FEEL: Fast, Energy-Efficient Localization for Autonomous Indoor Vehicles

Contact Info

Product

Resources

About