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I'd imagine 90% of the kalman filters out there are for 2 or maybe 3 dimensions, since the use case is mostly this, determining a position.

The filter fails is when there is not a single "true" answer to aim for, but there are many true answers. A position is clearly defined as long as it is not quantum physics.

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Yeah. Using the Kalman filter just to determine the position from noisy position measurements really undercuts the capability of the filter to use system physics to estimate the true state.

In one of the most common applications of Kalman filters, autonomous robots (e.g., a robot vacuum or a commercial drone), the filters are around 9 to 12 dimensions.

Right, in addition to the position you usually want the velocity, and sometimes also the acceleration, in all dimensions. More ambitious (or optimistic) practitioners could add more sensor measurements, like gyroscopes.

You are right of course and I was out of my depth. I wonder if the vector types now being added to databases for ML/AI stuff could help with this.

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