Point cloud registration for indoor mapping using time-of-flight camera

Kenta Ochiai, Masafumi Nakagawa

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

Abstract

Disaster monitoring requires for a safety and rapidity. 3D measurement, such as photogrammetry and laser scanning, can satisfy these requirements in a structure inspection and modeling. Aerial photogrammetry and laser scanning are applied to 3D data acquisition in damaged outdoor environments. Recently, the ground-based disaster monitoring also requires 3D data acquisition in indoor environment. We propose a point cloud data alignment methodology based on Iterative Closest Point (ICP) algorithm and SLAM approaches. However, conventional ICP and SLAM use only geometrical features. In other words, we are difficult to align simple planes. Thus, image matching using intensity values taken from TOF camera are integrated into a feature matching for a stable 3D data alignment.

Original languageEnglish
Title of host publication34th Asian Conference on Remote Sensing 2013, ACRS 2013
PublisherAsian Association on Remote Sensing
Pages44-49
Number of pages6
ISBN (Print)9781629939100
Publication statusPublished - 2013 Jan 1
Event34th Asian Conference on Remote Sensing 2013, ACRS 2013 - Bali, Indonesia
Duration: 2013 Oct 202013 Oct 24

Publication series

Name34th Asian Conference on Remote Sensing 2013, ACRS 2013
Volume1

Conference

Conference34th Asian Conference on Remote Sensing 2013, ACRS 2013
CountryIndonesia
CityBali
Period13/10/2013/10/24

Keywords

  • Flat surface
  • Handheld 3D scanner
  • Iterative closest point (ICP)
  • Simultaneous localization and mapping (SLAM)

ASJC Scopus subject areas

  • Computer Networks and Communications

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  • Cite this

    Ochiai, K., & Nakagawa, M. (2013). Point cloud registration for indoor mapping using time-of-flight camera. In 34th Asian Conference on Remote Sensing 2013, ACRS 2013 (pp. 44-49). (34th Asian Conference on Remote Sensing 2013, ACRS 2013; Vol. 1). Asian Association on Remote Sensing.