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.

利用雷达技术进行航空点云的语义分割，同时识别多个碰撞威胁，提高无人机的环境感知能力和安全性。

利用 PointNet 在空中雷达点云中对无人机碰撞危险进行分割

Segmentation of Drone Collision Hazards in Airborne RADAR Point Clouds  Using PointNet

As the demand for autonomous navigation in off-road environments increases,
the need for effective solutions to understand these surroundings becomes
essential. In this study, we confront the inherent complexities of semantic
segmentation in RADAR data for off-road scenarios. We present a novel pipeline
that utilizes LIDAR data and an existing annotated off-road LIDAR dataset for
generating RADAR labels, in which the RADAR data are represented as images.
Validated with real-world datasets, our pragmatic approach underscores the
potential of RADAR technology for navigation applications in off-road
environments.

在该研究中，我们使用 LIDAR 数据和已标注的离路 LIDAR 数据集来生成 RADAR 标签，并将 RADAR 数据表示为图像，从而应对离路情景下 RADAR 数据的语义分割的固有复杂性。通过实际数据集的验证，我们的实用方法突显了 RADAR 技术在离路环境导航应用中的潜力。