Sensor Fusion Is a World-Model Problem, Not a Filtering Problem
The textbook framing of sensor fusion is statistical: combine noisy measurements to estimate a state with lower variance. That framing is necessary but badly incomplete for a robot that has to act.
What planning and control actually need is a single, temporally consistent world model: where surfaces are, what is static versus dynamic, what is traversable, and how confident the system is about each of those claims. Fusion is the process of maintaining that model, not just smoothing a signal.
When fusion is treated as a world-model problem, disagreement between sensors becomes information rather than noise to be averaged away. A radar return with no visual support might be a real object the camera missed, and the right response is caution, not suppression.
ApexX builds the world model as the central artifact of the system. Every other module reads from it, and its calibrated uncertainty is what lets the controller stay both safe and fast.
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