- Built a frontend visualization tool using Open3D, Python to render 3D point cloud with intesity and classification

- Built a background map to isolate moving and non-moving in complex point cloud

- Developed a system to analyze and visualize drone swarm behavior using LiDAR data, enabling real-time monitoring and threat assessment

- Used DBSCAN to cluster point cloud data and identify individual drones, then applied Kalman filter to track their trajectories and predict future positions

- Use PointNet to classify drone types based on their 3D shapes generated from the point cloud data + IR camera signatures

Demonstration Videos

Official Website

Visit www.airspaceforensics.com 🡥