A Kalman filter solves the specific problem of estimating a finite-dimensional state vector from uncertain measurements, where the measurement errors and their correlations follow (or can be approximated as) a multivariate Gaussian.

That works quite well for, say, combining GPS (which has short-term noise) with inertial/odometry measurements (which suffer from long-term drift) to determine your vehicle's position, orientation, and velocity in 3D space (expressible as a 9-dimensional state vector). But it's not directly applicable to problems like combining map data with LIDAR and vision to generate a representation of your surroundings.

That's informative, thanks. I was under the impression that autonomous cars already combine all their sensor readings

“Sensor fusion” isn’t just a thing, it’s an entire area of study. Kalman filtering is one technique in the umbrella of sensor fusion. There is a lot of room for improvement here.

"Lichess is for professionals. Plenty of GMs ims and masters play on it. Even Magnus plays the tournaments. This is like saying Linux is not enough for computer professionals."

This is a misunderstanding. Lichess is used by chess professionals for playing online chess against each other (which is mostly recreation and not serious chess preparation). Software like ChessBase is used for game preparation.