Map building of unknown environment using PSO-tuned enhanced Iterative Closest Point algorithm

Chen Chien Hsu, Hua En Chang, Yin Yu Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Iterative Closest Point (ICP) algorithm is widely used in 2D and 3D spatial and geometric alignment. There are many variants of the ICP algorithm, proposing methods to minimize the sum of Euclidean distances between two clouds of scanning points for map building of an unknown environment by a mobile robot. Considering simplicity and computational efficiency, this paper proposes an enhanced-ICP incorporating a Particle Swarm Optimization (PSO) to effectively filter out outliers and avoid the false matching points during the map building process. Experimental results showed that, the proposed PSO-tuned enhanced-ICP can effectively reduce the accumulated errors to improve the map building accuracy by circumventing the problems of local optimal solutions resulted from the outliers and false matching points during the map building process.

Original languageEnglish
Title of host publicationICSSE 2013 - IEEE International Conference on System Science and Engineering, Proceedings
Pages279-284
Number of pages6
DOIs
Publication statusPublished - 2013
EventIEEE International Conference on System Science and Engineering, ICSSE 2013 - Budapest, Hungary
Duration: 2013 Jul 42013 Jul 6

Publication series

NameICSSE 2013 - IEEE International Conference on System Science and Engineering, Proceedings

Other

OtherIEEE International Conference on System Science and Engineering, ICSSE 2013
Country/TerritoryHungary
CityBudapest
Period2013/07/042013/07/06

Keywords

  • Iterative Closest Point
  • Map Building
  • Particle Swarm Optimization

ASJC Scopus subject areas

  • Control and Systems Engineering

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