Analysis of different feature selection criteria based on a covariance convergence perspective for a SLAM algorithm

Fernando A. Auat Cheein, Ricardo Carelli

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)62-89
Number of pages28
JournalSensors
Volume11
Issue number1
DOIs
Publication statusPublished - Jan 2011

Keywords

  • Features selection
  • Mapping
  • SLAM

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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