STARDUST

Synthetic data for off-road autonomy

Unstructured-terrain perception across occlusion, dust, and degraded visibility. Generate the terrain and conditions that break perception, before they break it in the field.

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Endless terrain, before the field tests you.

Every scene off-road is a new scene

Unstructured terrain has no lane lines and no map. Dust, mud, smoke, and low light defeat EO-only perception, and the terrain that matters most is remote, dangerous, and expensive to reach.

Stardust generates endless terrain, vegetation, and degraded-visibility conditions across EO, IR, LiDAR, and thermal, with point and pixel-level ground truth, so off-road perception and locomotion are tested before the field tests them.

Why real data falls short

// collecting off-road today

  • ✕Unstructured terrain has no lane lines and no map, every scene is different
  • ✕Dust, mud, and degraded visibility defeat EO-only perception
  • ✕The terrain that matters most is remote, dangerous, and expensive to reach

Sensor-true data, perfectly labeled

Unstructured terrainGenerate endless variation in terrain, vegetation, and obstacles.
Degraded visibilityDust, mud, rain, smoke, and low light across EO, IR, and thermal.
LiDAR and thermal labelsRegistered multi-sensor data with pixel and point-level ground truth.
SENSOR COVERAGE
EOIRLIDARTHERMAL

What teams build with it

Terrain classificationDrivable-surface and hazard segmentation off-road.
Obstacle detectionDetect rocks, ditches, and vegetation in clutter.
Occlusion handlingPerceive through dust, foliage, and partial views.
Learned locomotionTrain policies for rough-terrain navigation and control.

Speaks your domain

The vocabulary, sensors, and benchmarks off-road teams actually use.

unstructured terraintraversabilityocclusionLiDARthermaldegraded visibilityUGVlearned locomotionsemantic segmentationpoint cloud
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Evaluate off-road policies on Manifold

Off-road autonomy is increasingly learned policy. Once perception is trained on Stardust, evaluate locomotion and navigation policies on Manifold.

EXPLORE MANIFOLD

Questions teams ask

How do you train off-road perception without field collection?

Generate endless unstructured terrain across dust, mud, smoke, and low light, with segmentation and point-level ground truth, then validate before you deploy.

Which sensors does off-road perception need?

EO alone is not enough off-road. Stardust generates registered EO, IR, LiDAR, and thermal so perception holds up in degraded visibility.

Can you train learned locomotion?

Yes. Train perception on Stardust, then evaluate locomotion and navigation policies on Manifold across terrain you would never risk in the field.

Unlock unlimited off-road datasets

Tell us what you are building and the scenarios you need. We will get you access.

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