Fusion starts before the algorithm
Camera, thermal, event and inertial sensors observe the world through different sampling models. A fusion algorithm can only combine them correctly when the electronics preserve when each observation was made and how the sensors are positioned relative to one another.
Build a shared time model
Hardware triggers, disciplined clocks and ingress timestamps each solve different parts of the problem. The architecture should define clock sources, synchronization error, timestamp location and behavior after restart or link interruption. Measuring time uncertainty is more useful than assuming every interface is synchronized.
Treat calibration as managed configuration
Intrinsic calibration describes an individual sensor; extrinsic calibration relates sensors to each other and to the platform. Temperature, mechanical service and replacement can change those relationships. Calibration records therefore need identifiers, versions and validity checks that travel with the data.
Partition workloads by latency and context
Deterministic preprocessing and timestamp alignment may belong in an FPGA or real-time core, while feature extraction and inference run on an NPU or application processor. Keeping raw evidence, health data and selected intermediate results makes fusion decisions observable rather than opaque.
| Architecture layer | Responsibility |
|---|---|
| Sensor boundary | Capture, trigger and timestamp close to acquisition. |
| Calibration | Maintain intrinsic, extrinsic and timing relationships. |
| Compute partition | Place deterministic and AI workloads on suitable resources. |
| Evidence trail | Retain health, timing quality and decision context. |
Apply the architecture to a real platform.
Share the interfaces, environment, data rates and decision metric. We will map the engineering trade-offs to a practical system configuration.
Discuss your constraints