Korean Journal of Construction Engineering and Management (한국건설관리학회논문집)

Korea Institute of Construction Engineering and Management (한국건설관리학회)
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Scan Matching based De-skewing Algorithm for 2D Indoor PCD captured from Mobile Laser Scanning

스캔 매칭 기반 실내 2차원 PCD de-skewing 알고리즘

  • Kang, Nam-woo (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Sa, Se-Won (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Ryu, Min Woo (HanmiGlobal) ;
  • Oh, Sangmin (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Lee, Chanwoo (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Cho, Hunhee (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Park, Insung (School of Civil, Environmental and Architectural Engineering, Korea University)
  • 강남우 (고려대학교 건축사회환경공학과) ;
  • 사세원 (고려대학교 건축사회환경공학과) ;
  • 류민우 (한미글로벌) ;
  • 오상민 (고려대학교 건축사회환경공학과) ;
  • 이찬우 (고려대학교 건축사회환경공학과) ;
  • 조훈희 (고려대학교 건축사회환경공학과) ;
  • 박인성 (고려대학교 건축사회환경공학과)
  • Received : 2021.02.06
  • Accepted : 2021.04.26
  • Published : 2021.05.31
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Abstract

MLS (Mobile Laser Scanning) which is a scanning method done by moving the LiDAR (Light Detection and Ranging) is widely employed to capture indoor PCD (Point Cloud Data) for floor plan generation in the AEC (Architecture, Engineering, and Construction) industry. The movement and rotation of LiDAR in the scanning phase cause deformation (i.e. skew) of PCD and impose a significant impact on quality of output. Thus, a de-skewing method is required to increase the accuracy of geometric representation. De-skewing methods which use position and pose information of LiDAR collected by IMU (Inertial Measurement Unit) have been mainly developed to refine the PCD. However, the existing methods have limitations on de-skewing PCD without IMU. In this study, a novel algorithm for de-skewing 2D PCD captured from MLS without IMU is presented. The algorithm de-skews PCD using scan matching between points captured from adjacent scan positions. Based on the comparison of the deskewed floor plan with the benchmark derived from TLS (Terrestrial Laser Scanning), the performance of proposed algorithm is verified by reducing the average mismatched area 49.82%. The result of this study shows that the accurate floor plan is generated by the de-skewing algorithm without IMU.

실내 도면 획득을 위해 실내 형상정보를 습득할 수 있는 MLS (Mobile Laser Scanning)가 건설업에서 주목받고 있다. MLS의 특성상 스캐닝 중 LiDAR (Light Detection and Ranging)의 움직임을 발생하며, 이로 인해 습득된 포인트가 왜곡되는 skew가 발생한다. 이러한 skew를 보정하고 정확한 형상정보를 획득하기 위해 관성측정장치를 활용한 de-skewing 기법에 관한 연구가 진행되고 있다. 하지만, 해당 연구들은 관성측정장치를 활용하기 어려운 환경에서 사용하기 어려운 한계점이 있다. 이에 본 연구에서는 MLS로 습득한 실내 2차원 PCD (Point Cloud Data)를 대상으로 관성측정장치를 사용하지 않은 de-skewing 기법을 제시하였다. 해당 알고리즘은 인접한 스캔 지점의 포인트 간의 스캔 매칭을 통해 skew를 보정하였다. TLS (Terrestrial Laser Scanning)로 습득한 기준 데이터와 본 알고리즘을 통해 de-skewing을 진행한 데이터를 비교하여 검증하였으며, 모든 조건에서 면적 오차를 평균 49.82% 감소하여 본 알고리즘을 통해 관성측정장치 없이 정확한 실내 도면 도출이 가능함을 보였다.

Keywords

Acknowledgement

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. 2018R1A4A1026027).

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