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Segmentation of drone collision hazards in airborne RADAR point clouds using PointNet
Date
2024
Abstract
The integration of drones into shared airspace for beyond visual line of sight (BVLOS) operations presents significant challenges but holds transformative potential for sectors like transportation, construction, energy and defence. A prerequisite for this integration is equipping drones with enhanced situational awareness to ensure collision avoidance and safe operations. Current approaches mainly target single object detection or classification, or simpler sensing outputs that offer limited perceptual understanding and lack the rapid end-to-end processing needed to convert sensor data into safety-critical insights. In contrast, our study leverages radar technology for novel end-to-end semantic segmentation of aerial point clouds to simultaneously identify multiple collision hazards. By adapting and optimizing the PointNet architecture and integrating aerial domain insights, our framework distinguishes five distinct classes: mobile targets like drones (DJI M300 and DJI Mini) and airplanes (Ikarus C42), and static returns (ground and infrastructure) which results in enhanced situational awareness for drones. To our knowledge, this is the first approach addressing simultaneous identification of multiple collision threats in an aerial setting, achieving a robust 94% accuracy. This work highlights the potential of radar technology to advance situational awareness in drones, facilitating safe and efficient BVLOS operations.
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Description
Publisher
Institute of Electrical and Electronics Engineers
Citation
IEEE Transactions on Intelligent Transportation Systems, 2024
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Funding Information
This work was funded by The Data Lab, Codeplay Software Ltd., and University of the West of Scotland under grant number Reg-201629 Intelligent RADAR Perception to Improve the Safety of Autonomous Vehicles (“The Data Lab Industrial Doctorate”), and by the European Commission under project RAPID (Risk-Aware Port Inspection Drones, 2020-2023) which is funded through the Horizon 2020 research and innovation programme and Grant Agreement number 861211