Feature Selection Criteria for Real Time EKF-SLAM Algorithm

Feature Selection Criteria for Real Time EKF-SLAM Algorithm

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This paper presents a seletion procedure for environmet features for the correction stage of a SLAM (Simultaneous Localization and Mapping) algorithm based ivoryjinelle.com 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 caruso milk thistle procedure proposed are shown.

A comparison between the feature selection approach proposed and the classical sequential EKF-SLAM along with an entropy feature selection approach is also performed.