One trustworthy signal built from many imperfect sensors

The foundation is time and space alignment: hardware timestamps and PTP synchronization so measurements from different sensors describe the same instant, and extrinsic calibration so they describe the same coordinate frame — fusing misaligned data produces confident nonsense. On that foundation sit the estimators: Kalman filters and their extended and unscented variants for tracking and localization, complementary filters where dynamics are simple, and factor-graph methods where trajectories matter. Outlier gating rejects the glitch measurements every real sensor produces, and covariance tuning is done against recorded data, not intuition.

Production readiness means testing against ground truth and, just as important, engineering the degraded modes: what the system does when the camera is blinded or the GPS drops, and how it reports its own confidence. We build logging and replay tooling into every fusion system, because the ability to re-run a field incident through the filter on a bench is what makes these systems debuggable rather than mysterious.

Feasibility starts with a recording, not a filter. Before proposing an architecture we instrument the candidate sensors, capture data through the operating conditions that matter — the glare hour, the loaded vehicle, the aisle where the GPS drops — and analyze it offline. That data tells us which sensors carry information in which conditions, how their errors correlate, and what update rates and latencies the estimator will see. It also settles the architecture: a loosely coupled design that fuses each sensor's output is simpler and easier to debug, while tightly coupled fusion of raw measurements buys robustness at the cost of complexity. We choose the simplest structure that meets the requirement.

Implementation targets the compute you have — embedded processor, industrial PC, or microcontroller — and the filter is written for fixed-rate, bounded-latency execution rather than as a research script. Calibration does not stay put, because mounts shift, lenses get replaced, and wheels wear, so we build in health monitoring that watches innovation statistics and flags a sensor whose measurements have stopped agreeing with the rest, along with a documented re-calibration procedure. The system is handed over with source, the recorded datasets, the replay tooling, and a written description of the model and its tuning, so your engineers can extend it rather than treat it as a black box.