Computationally efficient mapping for a mobile robot with a downsampling method for the iterative closest point

Shodai Deguchi, Genya Ishigami

研究成果: Article査読

2 被引用数 (Scopus)

抄録

This paper proposes a computationally efficient method for generating a three-dimensional environment map and estimating robot position. The proposed method assumes that a laser range finder mounted on a mobile robot can be used to provide a set of point cloud data of an environment around the mobile robot. The proposed method then extracts typical feature points from the point cloud data using an intensity image taken by the laser range finder. Subsequently, feature points extracted from two or more different sets of point cloud data are correlated by the iterative closest point algorithm that matches the points between the sets, creating a large map of the environment as well as estimating robot location in the map. The proposed method maintains an accuracy of the mapping while reducing the computational cost by downsampling the points used for the iterative closest point. An experimental demonstration using a mobile robot test bed confirms the usefulness of the proposed method.

本文言語English
ページ(範囲)65-75
ページ数11
ジャーナルJournal of Robotics and Mechatronics
30
1
DOI
出版ステータスPublished - 2018 2月

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 電子工学および電気工学

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