This paper presents Baby Walker a seletion procedure for environmet features for the correction stage of a SLAM (Simultaneous Localization and Mapping) algorithm based on an Extended Kalman Filter (EKF).This approach decreases the computational time of the correction stage which allows for real and constant-time implementations of the SLAM.The selection procedure consists in chosing the features the SLAM system state covariance is more sensible to.The entire system is implemented on a mobile robot equipped with a range sensor laser.The features extracted from the environment correspond to lines and corners.
Experimental results of the real time SLAM algorithm and an analysis of the processing-time consumed by the SLAM with the feature selection procedure proposed are shown.A comparison between the feature selection approach proposed and the classical sequential EKF-SLAM along with TROUSERS SPLIT SUIT HOPKINS an entropy feature selection approach is also performed.