Segmentation of Drone Collision Hazards in Airborne RADAR Point Clouds
  Using PointNet
Hector Arroyo Paul Kier Dylan Angus Santiago Matalonga Svetlozar Georgiev Mehdi Goli Gerard Dooly James Riordan
The integration of unmanned aerial vehicles (UAVs) 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 defense. A critical prerequisite for this integration
is equipping UAVs with enhanced situational awareness to ensure 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 drones (DJI M300 and DJI Mini) and
airplanes (Ikarus C42), and static returns (ground and infrastructure) which
results in enhanced situational awareness for UAVs. 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 UAVs, facilitating safe and efficient BVLOS operations.