LIDAR + CAMERA + RADAR + IMU
Multi-modal annotation with calibration-verified cross-modality consistency. Single 3D label projects to every camera, every radar return, every thermal frame.
Cross-modal consistency PTP-synchronised
Separate per-sensor jobs can look complete and still disagree in the fused frame.
YPAI draws one object in the primary sensor and projects it through verified extrinsics onto every secondary stream. The annotator confirms the landing before the label is closed.
pickup · occluded One camera, fully labelled Twenty objects across seven classes, with occlusion and truncation recorded. Extent, class and identity are all a single camera can give. Depth comes from the sensors below.
The cuboid lands on the pixels that actually belong to the object, including truncated and night frames.
The same identity carries Doppler velocity where radar is in the stack.
The silhouette holds when RGB fails. Heat-only classes stay available when the taxonomy needs them.
Illustrative readout. The engagement sets the primary sensor, the projection policy and the drift threshold the delivery must report.
Paying for isolated LiDAR boxes and isolated camera boxes is the expensive way to discover they do not occupy the same object.
Most common stack
Geometry from LiDAR, class and attributes from camera. One 3D box is projected into every camera view through verified extrinsics.
Weather-robust stack
The same identity also carries Doppler velocity. Radar keeps the object when rain, spray or darkness take the camera down.
Geo-localised stack
Point clouds are labelled in a trajectory that stays georeferenced. The map and the object share one coordinate frame.
Low-light stack
RGB and long-wave thermal share one identity. When the camera goes dark, the thermal silhouette is still a labelled object.
Surround stack
Identity continues as the object leaves one camera and enters the next. The handoff is a labelled event, not a new object.
Per-sensor annotation cannot settle a disagreement between streams. Fusion-aligned labels can.
Vehicle, pedestrian, cyclist and lane labels share one identity across LiDAR, camera and radar. Taxonomy can follow nuScenes or Waymo schemas when the engagement asks for that alignment.
LiDAR + camera + IMU for indoor and outdoor SLAM. Dynamic people, navigable floor and graspable objects are labelled against one trajectory.
Multi-camera plus thermal. Cross-camera re-identification is a labelled event. Day and night coverage share the same person ID. The same calibrated multi-sensor stack extends to defence and national-security perimeter perception.
LiDAR + IMU + GPS + camera. Delivery can include LAS / LAZ and a georeferencing check. BIM-ready when the engagement specifies it.
Schema, projection policy and a shared subset all serve one decision: is the multi-sensor contract stable enough to scale?
Class taxonomy is locked. Per-sensor intrinsics, pairwise extrinsics and PTP timestamps are verified before anyone draws a production label.
Output. Schema plus calibration verification record.
Visibility rules per sensor. Which stream is primary for each class, and which stream is allowed to correct it.
Output. Versioned annotation guideline.
A shared subset is labelled in the fused viewer. Cross-modal agreement and projection error are measured. The guideline is revised on what the round actually found.
Output. Cross-modal agreement record and revision decisions.
Production starts when the calibration round clears the task-specific projection and agreement thresholds in the acceptance plan.
The gate is a recorded decision. Drift after this point is reported.
Output. Signed record that the gate was cleared.
One 3D label in the primary sensor, projected to every secondary. The annotator validates the landing. Work runs in the proprietary fused viewer; tenant isolation is specified per project in the security annex.
Output. Multi-modal annotations.
Per-class scores per modality, projection-error report, time-sync drift audit, Article 30 records and a signed DPA.
Output. Final package plus multi-modal metrics.
These records travel with the labels. They are specified in the statement of work, not assembled afterwards.
Intrinsic and extrinsic checks per sensor, plus a PTP drift report across the recording.
The 3D box projected to every camera, radar return and thermal frame. Per-class projection error. Velocity delta where radar is present.
Per-class scores on each stream, identity preservation across sensor handoff, and a distance breakdown when the task needs it.
Frame-pair correspondence, interpolation policy for mismatched rates, and threshold flags.
Article 30 records, signed DPA, lawful-basis documentation, 30-day erasure terms, and the sub-processor list.
A short brief is enough to start.
YPAI will identify the projection policy, the open ontology decisions and the first calibration step.
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We reply inside one EU business day with the questions that must be resolved before a pilot or production scope.
Per-sensor intrinsics, pairwise extrinsics, and PTP-synchronised timestamps with sub-millisecond skew.
Calibration is verified before annotation. Drift is checked across the recording and reported at delivery.
A documented projection policy decides which sensor is primary for each class.
Annotators validate the projection before the label is finalised. Remaining disagreement is recorded in the delivery report.
Yes. Long-wave thermal, near-infrared and multi-spectral bands from drone or satellite capture.
Calibration is verified across those modalities. The taxonomy can include thermal-only classes such as hot-spot.
Norwegian company, EEA processing by default, EEA-only sourcing on request.
30-day GDPR Article 17 erasure SLA. Norwegian jurisdiction. Transfer controls defined per project.
nuScenes JSON, KITTI-360, AV2 SDK, and custom Protocol Buffers when the engagement specifies them.
Per-modality files plus a cross-modal manifest. Time-aligned multi-sensor packages.
Yes, when the engagement asks for alignment with corpora such as nuScenes, Waymo Open, Argoverse 2, A2D2 or KITTI-360.
Production work runs against your own streams under the engagement DPA. Public corpus licences are checked per project.
Brief the sensor mix. The reply names the projection policy and the first gate.
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We recruit contributors for this kind of work continuously. Openings are listed per country, with the rate stated in the country's own currency.
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