Validation infra for outdoor autonomy.
Arcland reconstructs real outdoor sites as sensor-accurate 3D Gaussian splat scenes, then runs your autonomy stack against them via ROS 2 and PX4.
BACKED BY
HOW IT WORKS
4 layers.
Capture, simulate, generate scenarios, mine failures, one integrated platform for the policies that learn outdoors.
Capture
01
Fly any off-the-shelf drone over a real outdoor site. We reconstruct it within hours as a sensor-accurate 3D Gaussian splat with collision geometry and semantic segmentation.
Simulation
02
Splats that respond correctly to depth cameras, LiDAR, thermal imagers, and event cameras, not just RGB. Validated sensor-level error models, not pretty pixels.
Scenarios
03
Lighting, weather, wind fields, dynamic obstacles, and adversarial agents generated at scale from a single seed scene. Distributions, not hand-authored corner cases.
Evaluation
04
Every policy version runs against the library overnight. Regressions are auto-classified as planner, perception, or control. CI for embodied policies.
Bridges
05
First-class integrations with ROS 2, PX4, and the VLA training frameworks teams actually use, LeRobot, OpenVLA, Octo. Drop-in for the existing stack.
WHO IT IS FOR
Built for the teams shipping outdoor autonomy.
VLA labs and foundation-model teams doing outdoor embodied AI. Commercial drone autonomy teams in inspection, forestry, and agriculture. University robotics labs building the next generation of outdoor policies.
FM Labs
Drone Autonomy
Agriculture
Defense
WHY ARCLAND
The infrastructure layer for outdoor embodied AI did not exist.
01
Architectural fit to the new wave of learning-based policies.
02
Capture as a first-class workflow.
03
Sensor fidelity over visual fidelity.
04
A scene library that compounds.
INTEGRATION
Drop into the autonomy stack you already have.
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GET IN TOUCH
The next decade of robotics is being written outdoors.
If you are building outdoor embodied AI, training VLA policies for unstructured environments, or operating drones at scale, we'd like to talk.