Aerial environments are visually complex
Drones operate in dense, varied scenes: urban canyons, warehouse interiors, forests, mountainsides. Each environment type needs thousands of unique, varied 3D assets.
Drone Simulation Environments
UAV autonomy training needs cluttered, realistic environments, buildings, trees, wires, delivery targets, dynamic obstacles. Rigyd generates physics-enabled OpenUSD assets compatible with Isaac Sim, AirSim, and Gazebo.
Updated 2026-05-08
The problem
Why existing workflows fall short.
Lightweight physics still needs accuracy
UAV dynamics are sensitive to aerodynamic interactions with obstacles. Mass distribution and collision-mesh detail affect propeller wash modeling and rotor-wake-aware path planning.
Perception stacks need semantic variety
Vision-based drone autonomy trains on diverse object classes. Lack of semantic and geometric diversity caps policy robustness the moment a drone enters a new environment.
How Rigyd helps
Automate the hard parts.
Dense environment catalogs
Convert entire 3D building, infrastructure, and landscape catalogs into SimReady assets, useful for urban delivery, infrastructure inspection, and search-and-rescue training.
Wire- and edge-aware collision geometry
Convex decomposition captures wing-catching edges, antenna-snagging protrusions, and wire-like thin geometry, critical for realistic collision-avoidance policy training.
Semantic labels for perception
Each asset ships with semantic labels (building, vegetation, vehicle, person), enabling perception-in-the-loop UAV policy training without a separate annotation pass.
97%
cost reduction vs hand-modeled drone environments
40%
better real-world performance with accurate physics
~5 min
per asset, from upload to simulation-ready
FAQ
Frequently asked questions
Is Rigyd compatible with Isaac Sim, AirSim, and Gazebo?
Yes. OpenUSD output imports into all three. Physics properties (mass, collision geometry, friction) are preserved, so the same asset runs in every major UAV simulation stack without re-export.
Can Rigyd capture wire-thin obstacles for collision-avoidance training?
Yes. Convex decomposition captures edges and thin protrusions that simple bounding boxes miss, critical for realistic wire, antenna, and branch collision scenarios in UAV policy training.
Are assets suitable for dense urban environment simulation?
Yes. Bulk conversion makes urban, warehouse, and landscape catalogs practical. Each asset ships with semantic labels for vision-based drone autonomy and perception-in-the-loop training.
Does Rigyd support AirSim and Microsoft Project AirSim?
Yes. Microsoft Project AirSim is USD-native and consumes Rigyd's OpenUSD output directly. Legacy AirSim runs on Unreal Engine assets; the OpenUSD output can be brought into Unreal via the USD importer (or exported to .fbx via community USD→FBX tools where the workflow expects it). Semantic labels, collision meshes, and physics properties survive both pipelines, so drone policies can be trained on the same asset library across simulator versions.
Can Rigyd capture thin geometry like power lines, antennas, and chain-link fences?
Yes, these are critical for drone collision avoidance training. Convex decomposition handles cylindrical thin geometry (wires, antennas) with elongated hulls that preserve wing-catching edges. For chain-link or grid structures, Rigyd offers a "thin obstacle" mode that uses capsule chains instead of decomposition, ensuring the simulator can detect contact along the entire wire rather than just at bounding-box boundaries.
Build drone training environments at scale
Generate SimReady obstacle and environment assets from 3D models, images, or text for UAV simulation.