Localization of a Four-Wheeled Omnidirectional Mobile Robot Using Sensor Data: A Kalman Filter Approach

Abstract

In this paper, a model-based method to estimate the position of an omnidirectional mobile is proposed using a Kalman filter. The state variables of a four-wheeled omnidirectional mobile robot are estimated and are used for a feedback loop for trajectory control of the robot. Due to noise in the sensor measurement, the signal cannot directly be used for feedback closed-loop control. The system control without filter mode is not favourable as it produces significant tracking error and leads to high fluctuation in control input. The proposed method is demonstrated using numerical simulations on an omnidirectional mobile platform. Results are presented for different measurement sets. Effects of measurement noise level, filter parameters and modelling error (process noise covariance) are also presented, and it is observed that the position parameter estimation is robust with respect to measurement noise.