Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm

Abstract

This paper introduces several non-arbitrary features selection techniques for aSimultaneous Localization and Mapping (SLAM) algorithm. The features selection criteriaare based on the determination of the most significant features from a SLAM convergenceperspective. The SLAM algorithm implemented in this work is a sequential EKF (ExtendedKalman filter) SLAM. The features selection criteria are applied on the correction stage ofthe SLAM algorithm, restricting it to correct the SLAM algorithm with the most significantfeatures. This restriction also causes a decrement in the processing time of the SLAM.Several experiments with a mobile robot are shown in this work. The experiments concernthe maps reconstruction and a comparison between the different proposed techniques performance.The experiments were carried out at an outdoor environment composed by trees,although the results shown herein are not restricted to a special type of features.