Drone

The sky is the limit for test flights

Airspace regulations, GPS-denied environments, and fast-changing terrain make real-world drone testing slow, expensive, and incomplete. Deterministic sensor simulation lets you test beyond flight-hour limits.

Software augmented testing for drone perception systems.

Real-fidelity neural reconstructions and deterministic sensor simulation enable safe, scalable testing of drone perception across every altitude, terrain, and flight condition.

Challenges with Drone Perception Development

Safe, Reliable Perception

Edge Cases – Unique scenarios are critical for model accuracy and safety, but rarely seen in the real-world and often unsafe or impossible to capture

Observability – Changes to a perception model can have unintended consequences that are not observable until tested in the real-world

Safety – Crashes can lead to litigation, regulatory scrutiny, and diminish brand perception

Solution – Continuously evaluate both standard and edge-case scenarios in a virtual world designed to mimic the real-world

Time, and Cost

New drone sensor rigs, and sensor types require recapturing sensor data, labeling, curation, and QA

Entering new geographic markets requires new data and overcoming regulatory hurdles

Regulation is constantly changing and evolving requiring rework to perception models

Solution – Generate the scenarios, test suites, and datasets you need in days, not months

Scalability Across Airframes and Mission Profiles

New drone sensor rigs, and sensor types require recapturing sensor data, labeling, curation, and QA

Entering new geographic markets requires new data and overcoming regulatory hurdles

Regulation is constantly changing and evolving requiring rework to perception models

Solution – Simulate new camera configurations, environments, and scenarios by updating code, not recapturing data

PD Solutions

PD Replica + PD Sim for drone perception across every flight regime.

Evaluate

Open-loop and closed-loop testing integration for drone perception stacks. Nightly regression testing for terrain mapping, obstacle avoidance, and flight path planning. Perception unit testing across altitude, wind, and GPS conditions. Near-validation testing in real-world scanned environments (PD Replica).

Analyze

Benchmark flight scenarios and payload configurations across terrain types. Explore altitude and wind trade-offs for sensor placement optimization. Validate perception across urban delivery corridors, agricultural survey routes, and infrastructure inspection paths.

Train

Scaled data generation for terrain detection, obstacle segmentation, and landing zone identification. Infinite altitude and terrain variations across seasons, weather, and time of day. Simulate in real-world location scans for mission-specific training data (PD Replica).

PD Replica - Closing the sim-to-real gap with real environments

Incorporate real-world scans as fully annotated, simulation-ready environments seamlessly integrated into Parallel Domain’s Data Lab API. Experience unparalleled variety and realism for model testing, training, and validation

Addressing Industry Challenges

Test beyond flight-hour limitations

Simulate perfectly annotated sensor data in weeks rather than capturing, labeling, curating, and QAing real-world data that takes months. Run infinite variations of new scenario simulations with just a few lines of code.

Scale across airframes and mission profiles

Enter new markets more quickly by updating simulation assets and environments to match new needs. No need to recapture data when hardware changes; it is just a few small tweaks to our simulation sensor configurations.

Ensure safe operation in shared airspace

Ensuring safety and reliability comes from routinely testing your AI system against critical, rare, and exhaustive scenarios. Doing this across thousands of variations, in real or procedural environments provides additional assurance of systems performing as designed.

Schedule a Demo

See how PD can accelerate your drone perception development with high-fidelity synthetic data and deterministic sensor simulation.