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arXiv · 2310.09589

Real-Time Sense and Detect of Drones Using Deep Learning and Airborne LiDAR

Manduhu Manduhu·Alexander Dow·Petar Trslic·Gerard Dooly·Benjamin Blanck·James Riordan
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Abstract

The safe operation of drone swarms beyond visual line of sight requires multiple safeguards to mitigate the risk of collision between drones flying in close-proximity scenarios. Cooperative navigation and flight coordination strategies that rely on pre-planned trajectories, constant %{satellite and network connectivity and reliable Global Navigation Satellite System (GNSS) positioning are brittle to failure. Drone embedded sense and detect offers a comprehensive mode of separation between drones for deconfliction and collision avoidance. This paper presents the first airborne LiDAR based solution for drone-swarm detection and localization using 3D deep learning model. It adapts an existing deep learning neural network to the air-to-air drone scenario by expanding the scan space vertically. A new sparse convolution is proposed and applied to accelerate the backbone layer, which is the most time-consuming part of the neural network. To collect training data of safety critical, close-proximity multi-drone operations, a scenario Digital Twin is used to augment real datasets with high fidelity synthetic data. The trained model achieves over 80% recall and 96% precision when tested on real-world datasets. By incorporating a tracking-by-detection algorithm the system can reliably monitor the separation distance of multiple drones in challenging environments.

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Manduhu Manduhu, Alexander Dow, Petar Trslic, Gerard Dooly, Benjamin Blanck, James Riordan. 2023-10-14. Real-Time Sense and Detect of Drones Using Deep Learning and Airborne LiDAR. https://arxiv.org/abs/2310.09589

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