The Sensor Fusion Layer is the stratum that combines data from multiple sensors into a single, more reliable estimate of state. It sits above the Hardware sensing devices and below the Perception Layer that interprets the fused result. It contains alignment, filtering, and fusion algorithms together with uncertainty models.
Semantic Classification
Content
- The Sensor Fusion Layer merges readings from heterogeneous sensors to produce estimates more accurate and robust than any single source. Typical members include temporal and spatial alignment, recursive filters, data association, and the uncertainty models that weight each input. Its output is a consolidated state estimate with quantified confidence.
- It requires the Hardware Layer that supplies raw sensor streams, and it enables the Perception Layer, which interprets the fused estimate into a scene description. Reducing noise and resolving conflicts here improves every downstream decision.
- The layer bridges to the Kalman filter and Bayesian inference, the methods that combine measurements while tracking uncertainty. Correct uncertainty handling here is what lets higher layers reason safely about an imperfect world.