ROBOTICS & AVs
Sensor Fusion Labeling
Sensor Fusion Labeling. What it takes to run it.
Synchronized annotation for camera, LiDAR, and radar data streams.
// 01 · The question
“Where is this object in three dimensions, according to every sensor at once?”
// 02 · Why it is hard
The sensors do not agree by default. Extrinsic calibration and timestamp alignment have to be right before a single label is drawn, and both degrade quietly over the life of a platform.
// 03 · What the labels have to be
3D cuboid
Decided before the first frame
A planner needs metric position and heading, not a rectangle on an image. The cuboid lives in the vehicle frame, so the same annotation is valid for camera, LiDAR and radar simultaneously and does not have to be reconciled afterwards.
This is the decision that is expensive to reverse. The geometry has to match what the answer contains, and finding out it does not means labelling the set a second time.
// 04 · How we run it
Three parts of one loop, on this job.
LexAnnotate
Pixel-Perfect Data
Pixel-level semantic segmentation, LiDAR point cloud annotation, sensor fusion labeling, and temporal event tracking for complex perception tasks. Expert annotators, accountable for every label.
LexInsight
Spatial Intelligence
Build spatial understanding from flat labels. Map object relationships, track movement patterns, identify zero-day anomalies that standard training data misses.
LexAlert
Perception Alerts
Continuously validate perception performance in production. Detect when sensor drift or environmental changes degrade navigation. Route failures back for retraining before they cause incidents.
// 05 · What breaks it after launch
A driver or firmware update that shifts a timestamp by tens of milliseconds puts every sensor slightly out of register at speed. Each stream looks correct on its own, and the fusion is wrong from the exact hour of the update.
It is not the only one that can get this use case, it is the one that usually gets it first. All ten conditions.
// 06 · In robotics
Dynamic Spatial Navigation
The Challenge
Flat bounding boxes failed to provide spatial context for autonomous material handling in shifting environments.
Our Execution
Deployed pixel-level semantic segmentation and temporal event tracking for high-fidelity spatial datasets.
The Result
Enabled autonomous systems to adapt to changing environments safely. Reduced manual overrides.
95%+
Navigation reliability in changing environments