A framework development for mapping and detecting changes in repeatedly collected massive point clouds

S. Yoon, S. Ju, S. Park, J. Heo

Research output: Contribution to conferencePaper

Abstract

On a construction site, progress deviations can cause fatal damages to the project and stakeholders. Therefore, accurately monitoring and managing the construction environment is essential. Efficiently detecting and recognizing the changes will be the key factor for monitor and management goals. In this research, we present a framework for detecting and recognizing changes in repeatedly collected, massive 3D point cloud data from a mobile laser scanning system. The framework mainly consists of three parts; 1) mapping system; 2) analysis system; 3) Hadoop platform. Collecting point clouds is repeatedly executed to detect the changes over different time epochs. For collecting point cloud data, a mobile laser scanning system was developed based on Robot Operating System (ROS). Detecting changes between repeatedly collected point clouds have been processed based on Hadoop platform. Finally, detected changes are then implemented to a semantic mapping process which is based on deep learning. Developed framework have potential for wide application in massive point cloud data processing, construction site monitoring, street level change detection, and facility management.

Original languageEnglish
Pages603-609
Number of pages7
Publication statusPublished - 2019 Jan 1
Event36th International Symposium on Automation and Robotics in Construction, ISARC 2019 - Banff, Canada
Duration: 2019 May 212019 May 24

Conference

Conference36th International Symposium on Automation and Robotics in Construction, ISARC 2019
CountryCanada
CityBanff
Period19/5/2119/5/24

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Building and Construction
  • Human-Computer Interaction

Cite this

Yoon, S., Ju, S., Park, S., & Heo, J. (2019). A framework development for mapping and detecting changes in repeatedly collected massive point clouds. 603-609. Paper presented at 36th International Symposium on Automation and Robotics in Construction, ISARC 2019, Banff, Canada.