Visualizing and Modeling Interior Spaces of Dangerous Structures Using LIDAR

Visualizing and Modeling Interior Spaces of Dangerous Structures Using LIDAR

N. Maerz T.K. Assebaum D. Williams K. Shea Y. Xi X. Li Y. Duan 

Missouri University of Science and Technology, Rolla, MO, USA

University of Missouri, Columbia, MO, USA

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LIght Detection and Ranging (LIDAR) scanning can be used to safely and remotely provide intelligence on the interior of dangerous structures for use by first responders that need to enter these structures. By scanning into structures through windows and other openings or moving the LIDAR scanning into the structure, in both cases carried by a remote controlled robotic crawler, the presence of dangerous items or personnel can be confirmed or denied. Entry and egress pathways can be determined in advance, and potential hiding/ambush locations identified. This paper describes an integrated system of a robotic crawler and LIDAR scanner. Both the scanner and the robot are wirelessly remote controlled from a single laptop computer. This includes navigation of the crawler with real-time video, self-leveling of the LIDAR platform, and the ability to raise the scanner up to heights of 2.5 m. Multiple scans can be taken from different angles to fill in detail and provide more complete coverage. These scans can quickly be registered to each other using user defined ‘pick points’, creating a single point cloud from multiple scans. Software has been developed to deconstruct the point clouds, and identify specific objects in the interior of the structure from the point cloud. Software has been developed to interactively visualize and walk through the modeled structures. Floor plans are automatically generated and a data export facility has been developed. Tests have been conducted on multiple structures, simulating many of the contingencies that a first responder would face.


Hazards, laser scanning, LIDAR, model, segmentation


[1] Fischler, M.A. & Bolles, R.C., Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM 24, pp. 381–395, 1981. doi:

[2] Comaniciu, D. & Meer, P., Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Learning, 24(5), pp. 603–619, 2002. doi:

[3] Hoppe, H., DeRose, T., Duchamp, T., McDonald, J.A. & Stuetzle, W., Surface reconstruction from unorganized points. SIGGRAPH 1992, pp. 71–78.

[4] Kruskal, J.B., On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical Society, 7(1), pp. 48–50, 1956. doi:

[5] Lempitsky, V.S. & Boykov, Y., Global Optimization for Shape Fitting. CVPR 2007.

[6] Boykov, Y. & Kolmogorov, V., Computing Geodesics and Minimal Surfaces via Graph Cuts. ICCV 2003, pp. 26–33.