Tracking a Holonomic Mobile Robot with Systematic Odometry Errors

This paper compares state estimators to improve the navigation of mobile robots using odometry. We present a discrete-time model for a three-wheeled omnidirectional robot, then we create a dataset to test the simulated robot under different sources of error for the embedded odometry navigation system. Our measurement system encodes the wheel's angles and then computes the velocities in each wheel to calculate the position and the robot heading. To increase the localization accuracy, the Kalman filter (KF), the Colored Measurement Noise Kalman Filter (CMNKF), and the Robust
$H_{\infty}$
Filter (RHInf) are used. It is shown that the navigation system developed is more accurate than the basic one employing the standard CMNKF and RHInf filter.