Laser-based geometrical modeling of large-scale architectural structures using co-operative multiple robots

Yukihiro Tobata, Ryo Kurazume, Yusuke Noda, Kai Lingemann, Yumi Iwashita, Tsutomu Hasegawa

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


For the construction of 3-D shape models of large-scale architectural structures using laser range finders, a number of range images are taken from different viewpoints around the targets. Next, the obtained images are normally aligned by post-processing procedures, such as the ICP algorithm. However, to obtain convergent results in the ICP algorithm and align these range images to their proper positions, the initial position of each range image needs to be manually aligned to roughly the correct position. This paper proposes a new measurement and modeling system using a group of multiple robots and an on-board laser range finder. Each measurement position is identified by a highly precise positioning technique called the Co-operative Positioning System (CPS), which utilizes the characteristics of the multiple-robot system. Therefore, the proposed system can construct 3-D shapes of large-scale architectural structures without any post-processing procedure or manual intervention. In addition, it is possible to register range images even if the number of measurements is few and there are only a few range images, for example, due to range images containing insufficient feature shapes or overlapping regions. Measurement experiments in unknown and large indoor/outdoor environments including a large hall, a building, an urban district, and a cultural heritage have been successfully carried out using the newly developed measurement system consisting of three mobile robots named CPS-V. Path generation experiments of the mobile robots based on the partially measured 3-D model are also presented.

Original languageEnglish
Pages (from-to)49-62
Number of pages14
JournalAutonomous Robots
Issue number1
Publication statusPublished - Jan 2012

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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