LiDAR & vision fusion

Unified multimodal perception

Dense clouds plus RGB cues

Spatiotemporal sync: shared time base and extrinsics align every cloud with its image frame.

Depth + texture fusion: combine geometric and appearance cues for harder scenes.

Detection / segmentation outputs for cranes, gates, and safety logic.

Open inference APIs for your runtime and retraining loops.

How it works

Synchronized capture

Common time base and extrinsics align every cloud with its image pair.
Feature- or decision-level fusion

Balance latency and accuracy per use case.
Deploy & active learning

Edge or server runtimes with pipelines to ingest hard examples.

System diagram

Harsh-scene robustness

Glare, dust, and partial occlusion handled with fused cues.
Tunable fusion

Shift emphasis between LiDAR- or vision-led stacks.
Reuse across scenes

Same framework extends to perimeter, loading, and mobility.
Engineering toolkit

Calibration, evaluation, and ops tooling for long-term iteration.

Share classes, frame rate, and compute—we size sensors and algorithm scope.