A KF based Localization Algorithm for Nonholonomic Mobile Robot

We address the localization problem in unstructured environments for a nonholonomic mobile robot equipped with exteroceptive sensors. The usual approach to robot localization is to adopt the Extended Kalman Filter (EKF), which results in a typical predictor-corrector structure. The use of the EKF, as opposed to the standard Kalman Filter, derives from the nonlinearity of the robot kinematic model as well as of the observation function. In order to avoid at least the first linearization error, we propose to take advantage of the possibility of transforming the original kinematic system in chained form through an appropriate feedback transformation. The obtained equations are closed-form integrable, thereby yielding a linear discrete-time model which provides exact odometric prediction and associated covariance. Another advantage of our approach is that the stochastic description of the noise in the chained-form coordinates is more natural. The performance of the proposed localization method is illustrated through simulations on a mobile robot having the kinematics of a unicycle and equipped with ultrasonic sensors.