Simulation and testing infrastructure for autonomy programs
Null Labs gives autonomy engineering teams the simulation environment and synthetic data pipeline they need to build reliable perception systems — without waiting for real-world data collection and reducing time-intensive field testing.
faster edge case coverage vs. real-world collection
physics-accurate sensor simulation
closed-loop integration with your autonomy stack
built for defense and maritime autonomy programs
Sensor Simulation & SITL Testing
Photorealistic EO simulation environments for software-in-the-loop testing. Validate your autonomy stack against degraded conditions, edge cases, and adversarial scenarios before hardware integration.
Sensor fidelity
Physics-accurate EO rendering with real-world degradation — atmospheric haze, sensor noise, motion blur, and lighting variation.
Closed-loop SITL
Direct integration with your autonomy stack. Run your software against simulated sensor feeds in real time with configurable scenario control.
Environment library
Maritime, aerial, and ground environments. Author custom scenarios or pull from our pre-built library for common test conditions.
Training data generation
Generate labeled sensor data at scale to train and fine-tune your perception models. Cover the long tail of conditions your real-world dataset doesn't reach.
- EO imagery with automatic annotation
- Domain randomization across sensor configs and conditions
- Drop-in integration with existing training pipelines
Autonomy validation suite
Structured test suites for autonomy programs — from unit-level perception regression to full mission rehearsal in simulation.
- Pre-built scenario libraries for maritime and aerial domains
- Pass/fail metrics tied to program requirements
- CI/CD integration for continuous validation
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Talk to an engineer, not a salesperson
Our team works directly with autonomy engineers. If you're building perception for a UAS, USV, or UGV program, we'll walk you through how Null Labs fits into your stack — no slide deck required.