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Carnegie Mellon Robotics Institute

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Abstract

This paper describes an automated method for creating accurate 2D floor plan models of building interiors. Our approach takes as input a 3D point cloud, which is obtained from laser scanners positioned at multiple locations in the facility. We use a histogram of height data to detect floor and ceiling data. We then project the remaining points onto a 2D ground plane and create a histogram of point density, from which line segments corresponding to walls are extracted using a Hough transform. Openings due to doors and windows arise implicitly from the modeling process. Our algorithm operates in unmodified, cluttered environments. We also propose a set of evaluation measures for objectively measuring floor plan modeling algorithm performance. Finally, we propose strategies for mitigating the effect of clutter through strategic selection of cross-sections containing minimal clutter. Our experiments on data from a 40-room school building achieve promising results.

Notes

Sponsor: This research was funded, in part, by the National Science Foundation (CMMI-0856558) and by the Pennsylvania Infrastructure Technology Alliance.

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): Vision and Mobile Robotics Lab and 3D Vision and Intelligent Systems Group

Associated Project(s): Automated Reverse Engineering of Buildings and Automated Floor Plan Modeling

Text Reference

Brian E. Okorn, Xuehan Xiong, Burcu Akinci, and Daniel Huber, "Toward Automated Modeling of Floor Plans," Proceedings of the Symposium on 3D Data Processing, Visualization and Transmission, May, 2010.

BibTeX Reference

@inproceedings{Okorn_2010_6627,
   author = "Brian E Okorn and Xuehan Xiong and Burcu Akinci and Daniel Huber",
   title = "Toward Automated Modeling of Floor Plans",
   booktitle = "Proceedings of the Symposium on 3D Data Processing, Visualization and Transmission",
   month = "May",
   year = "2010",
}

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