In this work a novel solution to the Simultaneous Localization and Mapping (SLAM) problem for a mobile robot moving in an unknown indoor environment is proposed. The algorithm uses an Extended Kalman filter and a set of polynomials to map the robot surrounding environment boundaries. The main idea behind the proposed SLAM solution is to use the SLAM landmark extraction process to map the environment boundaries shape and the Kalman filter to estimate boundaries position. The algorithm uses measurements taken from a set of distance sensors placed on the robot. The proposed method has been evaluated in both numerical and experimental tests obtaining satisfactory estimation and mapping results.