Synthetic flight hours for drone autonomy.
Environments for training and testing drone perception and navigation: flight physics, camera and sensor simulation, waypoint and mission logic, and scenario generation at scale.
The pieces we deliver.
Flight physics
Multirotor and fixed-wing dynamics with wind and disturbance models, tuned against real logs where available.
Sensor simulation
RGB, depth and segmentation cameras, ray-cast ranging, IMU and GPS noise models.
Missions and scenarios
Waypoint and area-coverage missions, procedural terrain and obstacles, weather and time-of-day variation.
Datasets and evaluation
Synchronised recording, ground-truth labels and repeatable test runs for perception and navigation stacks.
Plain process, working builds.
A short call and a written plan: what gets built, in what order, and what a first playable or runnable milestone looks like.
Weekly builds and written progress reports. Source in your repository from day one.
Documentation, profiling notes and a team that can keep going without us, or with us on retainer.
Where this comes from.
Flight dynamics and autonomous flight AI
The physics integration and autonomous aircraft behaviour behind our simulation work, which the drone environments build on directly.
Computer vision from scratch
Markerless motion capture, stereo-camera sensory substitution and webcam-only robot navigation, all in C++ and OpenCV.
Have a project in this direction?
Send a short note. We reply within a couple of days with questions or a first take on scope.