: An autonomous ground vehicle has various devices from where it collects data and performs an action. State
estimation with noise present in it is one of the key requirements for many real-time problems and engineering. State estimation
is an essential requirement for many real-life systems from local to multi-resource information integration. The Kalman filter
and its variability have been used successfully in solving state equity problems. The Kalman filter can be used to predict the next
set of actions our car will take based on the information received. In this paper we have implemented kalman filter for state
estimation and results are obtained.
