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Sensors 2011, 11(1), 62-89; doi:10.3390/s110100062

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

Instituto de Automatica, National University of San Juan, Av. Libertador Gral. San Martin 1109 Oeste, San Juan, Argentina
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Received: 13 October 2010 / Revised: 22 November 2010 / Accepted: 21 December 2010 / Published: 23 December 2010
(This article belongs to the Section Physical Sensors)
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Abstract

This paper introduces several non-arbitrary feature selection techniques for a Simultaneous Localization and Mapping (SLAM) algorithm. The feature selection criteria are based on the determination of the most significant features from a SLAM convergence perspective. The SLAM algorithm implemented in this work is a sequential EKF (Extended Kalman filter) SLAM. The feature selection criteria are applied on the correction stage of the SLAM algorithm, restricting it to correct the SLAM algorithm with the most significant features. 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 concern the map 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. View Full-Text
Keywords: SLAM; mapping; features selection SLAM; mapping; features selection
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Auat Cheein, F.A.; Carelli, R. Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm. Sensors 2011, 11, 62-89.

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