Development and experimental validation of an adaptive extended Kalman filter for the localization of mobile robots

Abstract

A basic requirement for an autonomous mobile robot is its capability to elaborate the sensor measures to localize itself with respect to a coordinate system. To this purpose, the data provided by odometric and sonar sensors are here fused together by means of an extended Kalman filter. The performance of the filter is improved by an online adjustment of the input and measurement noise covariances obtained by a suitably defined estimation algorithm.