Extended Kalman Filter Localization for a Two-Wheeled Differential Drive Mobile Robot

Finding and tracking the mobile robot's pose is called localization which is a key problem in making truly autonomous Robots. Kalman filter is the most well known mathematical tool that can be used for stochastic estimation from noisy sensor measurements. This paper presents design and implementation of an extended Kalman filter (EKF) for estimating the position and orientation of a two-wheeled differential drive mobile obot. Simulation results show the proper performance of the proposed filter.

Keywords:

Localization , Stochastic estimation , Extended Kalman filter (EKF) , Differential drive , Mobile robot