A Kalman filter solves the specific problem of estimating a finite-dimensional state vector from uncertain measurements, where the measurement errors and their

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.