A drone-based XR geospatial perception and reconstruction system.
XRDrone is a Python-to-Unity XR perception pipeline that processes video, runs the current detection and tracking logic, and packages the results into structured UDP JSON for Unity. The Python side is responsible for producing stable object information and the spatial data needed downstream, including the pose and registration components that keep detections consistent within the scene. Rather than treating each frame independently, the method emphasizes continuity across frames so Unity can work with persistent, meaningful scene updates instead of noisy per-frame outputs.
On the Unity side, the system consumes the incoming JSON stream and uses it to drive the XR scene in real time. Detected and tracked objects are updated in a shared spatial context, allowing the scene to reflect where things are and how they move with consistent alignment. In practice, the method is the integration of three parts working together: computer-vision inference in Python, structured UDP communication between systems, and Unity-based spatial rendering that uses tracking and pose information to represent the environment coherently in XR.
System demonstration using ArUco markers, a DJI Neo drone camera, Unity, and real-time 3D object registration.
Left: Unoptimized system.
Middle: Optimized with BoT-SORT tracking and pose solver/refinement combinations (80% confidence cap).
Right: Same optimization with no confidence filter.
Our working software can be found here: https://github.com/XRDrone
Special thanks to Prof. Raffaele De Amicis for project sponsorship and guidance, and to Oregon State University’s School of EECS for supporting the XRDrone Capstone.
MIT License
Copyright (c) 2025 Raffaele De Amicis, William Brennan, Troy Diaz, Balakrishna Thirumavalavan, Guillermo Morales
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