Fusion is mostly a timing and calibration problem wearing an algorithm's clothes. Before a camera, a LiDAR, a radar and an inertial unit can be combined into one confident statement about the world, they have to agree on when each measurement was taken and where each sensor is relative to the others. Faststream engineers that foundation first, then the estimator on top of it.
A camera frame timestamped 12 ms late, fused with a LiDAR sweep, places an object travelling at 15 metres per second about 18 centimetres from where it actually was. Do that consistently and the tracker learns a systematic bias it cannot recover from.
So synchronisation comes first — hardware trigger, a shared time base, and a documented latency budget for every sensor path. Then extrinsic calibration, so the transform between each sensor frame is known and monitored rather than assumed. Only then does the estimator design matter, and by that point most of the difficulty has already been removed.
| Sensor | Strength | Weakness | Contributes |
|---|---|---|---|
| Camera | Dense appearance, classification, colour, texture | Illumination dependent, no direct range | What the object is |
| LiDAR | Direct range and geometry, illumination independent | Sparse at distance, degraded by heavy precipitation and dust | Where it is and what shape |
| Radar | Direct radial velocity, robust in weather | Coarse angular resolution, multipath | How fast it is closing |
| Inertial (IMU) | High-rate ego motion, no external dependency | Drifts without correction | How the platform itself moved |
| GNSS | Absolute position | Outage in tunnels, canyons and indoors | Where the platform is on the map |
| Thermal | Contrast independent of visible light | Low resolution, no colour | Presence of warm bodies at night |
Rail corridor monitoring. LiDAR provides range and geometry independent of illumination; camera imagery supplies the classification that keeps the nuisance-alarm rate low enough for the system to stay switched on.
In-cab driver monitoring. Infrared imaging fused with vehicle signals, so a head-pose estimate is interpreted differently on a straight motorway than during a deliberate mirror check.
Industrial guidance. Vision and depth sensing combined for pose estimation, bin picking and assembly verification where a single modality leaves ambiguity.
Sensor fusion combines measurements from multiple sensor types into a single estimate that is more accurate or more robust than any one sensor alone. In practice it depends less on the fusion algorithm than on time synchronisation and calibration between the sensors.
Because a timing error becomes a position error proportional to relative velocity. A 12 ms offset on an object closing at 15 metres per second puts it 18 centimetres from where it was, and a consistent offset teaches a tracker a bias it cannot correct.
It depends on the failure modes that matter. Cameras classify well and give no direct range; LiDAR gives range and geometry regardless of illumination. Where a missed detection at night or in low contrast is unacceptable — rail corridors are the clear case — LiDAR earns its cost.
Degraded-mode behaviour is designed and tested rather than discovered. The system detects the loss, states reduced confidence explicitly, and continues in a defined mode instead of silently producing worse output.
Tell us the specification, the constraint and the deadline. Programmes that cross silicon, radio, embedded and AI are where Faststream is strongest.