Why to use Kalman filter ?
The sensor measurements are not generally accurate, they usually have unpredictable, random and uncertain error/variation in their measurements. What does that mean ? Consider a thermometer which reads/measures room temperature, In a room with steady/constant temperature the sensor outputs a varying temperature measurement within a given range, the readings will be scattered around the actual room temperature, this is because the sensors generally have a degree of uncertainty in their measurement.
In the image below the marked X’s demonstrates the sensor readings over a period of time. You can see the though the actual temperature of the room is constant, the readings are distributed over a range of values around the actual temperature.
If sensor measurements return uncertain measurements, how do we know the actual room temperature ?
We’ll, that’s where Kalman filters comes to your rescue. Kalman filter is an iterative mathematical approach which is used to quickly (with less number of actual readings and iterations) converge to a better estimation of the actual value (better than the sensor measurement itself). In this case using Kalman filter will quickly help us estimate the actual room temperature using the uncertain measurements. In the above you can see how Kalman filter technique helps to quickly approximate the actual room temperature by converging close to the actual value by making use of the uncertain sensor measurements.
The application of Kalman filters are wide, For example Similar issues too are found when Lidar or Radar measurements are used to object tracking in self driving cars, the right estimate of other vehicle on the road is critical for safe driving, Kalman filter are used here too for estimation the actual position and velocity of other vehicles on the road.