Extended and Unscented Kalman Filters for mobile robot localization and environment reconstruction

Abstract

In this work we compare the performance of two algorithms, respectively based on the Extended Kalman Filter and the Unscented Kalman Filter, for the mobile robot localization and environment reconstruction problem. The proposed algorithms do not require any assumption on the robot working space: they are driven only by the measurements taken using ultrasonic sensors located onboard the robot. We also devise a switching sensors activation policy, which allows energy saving still achieving accurate tracking and reliable mapping of the workspace. The results show that the two filters work comparably well, in spite of the superior theoretical properties of the Unscented Filter.