A polynomial based SLAM algorithm for mobile robots using ultrasonic sensors - Experimental results

Abstract

In this work the Simultaneous Localization And Mapping (SLAM) problem for a mobile robot placed in an unknown indoor environment is faced. The environment is modeled as a set of polynomials used as SLAM landmarks. A polynomial based mapping algorithm is proposed and used along with an Extended Kalman filter to yield a solution to the SLAM problem. The filter updates the robot position and orientation estimation and the environment mapping using sparse measurements taken from a set of on board ultrasonic sensors. The proposed algorithm has been evaluated in both numerical and experimental tests obtaining very good estimation and mapping results.