Semi-automated approach to indoor mapping for 3D as-built building information modeling

Abstract BIM (Building Information Modeling), chiefly in the form of a 3D “as-built” model or information-sharing platform, has emerged as a powerful solution to the dynamic requirements of AEC (Architecture, Engineering, and Construction). However, whereas fast and accurate reconstruction of building interiors is essential to any collaborative construction management process, manual creation and utilization of a 3D as-built model typically results in low productivity and erroneous modeling results. This paper proposes a semi-automated method that accounts for and resolves the major problems in the streamlined manual process of 3D as-built model creation in BIM. The method generates a 3D wireframe model combined with clutter data, which is then imported into a BIM tool for as-built modeling. The present study evaluates the proposed method by applying it to two typical rooms in a test building. The 3D as-built model was then subjected to an accuracy assessment using reference points acquired by a total station. The contributions of the proposed method, as compared with fully manual operation, are: (1) reduction of the huge data size of the original point clouds; (2) improvement of the productivity of as-built BIM creation with the aid of 3D wireframes; and (3) accuracy enhancement through a refinement process that entails segmentation and regularization. However, the proposed method is limited to building interiors consisting of planar structures; the modeling of detailed objects, such as windows and doors, unfortunately, still requires manual operation. Thus, further research on detail modeling and refinement is necessary in order to increase the method’s automation and enhancement of its overall suitability for mapping complex indoor environments.

[1]  Burcu Akinci,et al.  Automatic execution of workflows on laser-scanned data for extracting bridge surveying goals , 2012, Adv. Eng. Informatics.

[2]  Salman Azhar,et al.  Building Information Modeling (BIM): Trends, Benefits, Risks, and Challenges for the AEC Industry , 2011 .

[3]  Qing Zhu,et al.  Mathematical morphology-based generalization of complex 3D building models incorporating semantic relationships , 2012 .

[4]  Alexander Zipf,et al.  Generating web-based 3D City Models from OpenStreetMap: The current situation in Germany , 2010, Comput. Environ. Urban Syst..

[5]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

[6]  Jeong-Han Woo,et al.  Use of As-Built Building Information Modeling , 2010 .

[7]  Soohee Han,et al.  Automated and Efficient Method for Extraction of Tunnel Cross Sections Using Terrestrial Laser Scanned Data , 2013, J. Comput. Civ. Eng..

[8]  Enrique Valero,et al.  Automatic Method for Building Indoor Boundary Models from Dense Point Clouds Collected by Laser Scanners , 2012, Sensors.

[9]  Kiyun Yu,et al.  Parallel Processing Method for Airborne Laser Scanning Data Using a PC Cluster and a Virtual Grid , 2009, Sensors.

[10]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[11]  Frank Schultmann,et al.  Building Information Modeling (BIM) for existing buildings — Literature review and future needs , 2014 .

[12]  Feniosky Peña-Mora,et al.  Integrated Sequential As-Built and As-Planned Representation with D4AR Tools in Support of Decision-Making Tasks in the AEC/FM Industry , 2011 .

[13]  Giovanna Sansoni,et al.  State-of-The-Art and Applications of 3D Imaging Sensors in Industry, Cultural Heritage, Medicine, and Criminal Investigation , 2009, Sensors.

[14]  Richard Szeliski,et al.  Reconstructing building interiors from images , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[15]  A Budroni,et al.  Automatic 3D modelling of indoor Manhattan-world scenes from laser data , 2010 .

[16]  Qing Zhu,et al.  A semantics-constrained profiling approach to complex 3D city models , 2013, Comput. Environ. Urban Syst..

[17]  Lars Harrie,et al.  Detection and typification of linear structures for dynamic visualization of 3D city models , 2012, Comput. Environ. Urban Syst..

[18]  Jaehoon Jung,et al.  Productive modeling for development of as-built BIM of existing indoor structures , 2014 .

[19]  Yusuf Arayici,et al.  An approach for real world data modelling with the 3D terrestrial laser scanner for built environment , 2007 .

[20]  Dieter Fox,et al.  RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments , 2012, Int. J. Robotics Res..

[21]  Clyde R Greenwalt,et al.  PRINCIPLES OF ERROR THEORY AND CARTOGRAPHIC APPLICATIONS , 1962 .

[22]  Xiang Li,et al.  A grid graph-based model for the analysis of 2D indoor spaces , 2010, Comput. Environ. Urban Syst..

[23]  Robin Drogemuller,et al.  National Guidelines for Digital Modelling , 2009 .

[24]  Soohee Han,et al.  Productive high-complexity 3D city modeling with point clouds collected from terrestrial LiDAR , 2013, Comput. Environ. Urban Syst..

[25]  Burcu Akinci,et al.  Automatic Reconstruction of As-Built Building Information Models from Laser-Scanned Point Clouds: A Review of Related Techniques | NIST , 2010 .

[26]  Sisi Zlatanova,et al.  A BIM-Oriented Model for supporting indoor navigation requirements , 2013, Comput. Environ. Urban Syst..

[27]  Jürgen Döllner,et al.  Concepts and techniques for integration, analysis and visualization of massive 3D point clouds , 2014, Comput. Environ. Urban Syst..

[28]  Burcin Becerik-Gerber,et al.  Imaged-based verification of as-built documentation of operational buildings , 2012 .

[29]  Yusuf Arayici,et al.  Towards building information modelling for existing structures , 2008 .

[30]  Burcu Akinci,et al.  Automatic Creation of Semantically Rich 3D Building Models from Laser Scanner Data , 2013 .