The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIV-M-3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-3-2021, 133–137, 2021
https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-133-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-3-2021, 133–137, 2021
https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-133-2021

  10 Aug 2021

10 Aug 2021

MAPPING BUILDING INTERIORS WITH LIDAR: CLASSIFYING THE POINT CLOUD WITH ARCGIS

J. R. Parent1, C. Witharana2, and M. Bradley1 J. R. Parent et al.
  • 1Dept. of Nat. Res. Sci., University of Rhode Island, Kingston, USA
  • 2Dept. of Nat. Res. and the Env., University of Connecticut, Storrs, CT, USA

Keywords: Lidar, indoor, interior, building, point cloud, classification

Abstract. Accurate maps of building interiors are needed to support location-based services, plan for emergencies, and manage facilities. However, suitable maps to meet these needs are not available for many buildings. Handheld LiDAR scanners provide an effective tool to collect data for indoor mapping but there are no well-established methods for classifying features in indoor point clouds. The goal of this research was to develop an efficient manual procedure for classifying indoor point clouds to represent features-of-interest.

We used Paracosm’s PX-80 handheld LiDAR scanner to collect point cloud and image data for 11 buildings, which encompassed a variety of architectures. ESRI’s ArcGIS Desktop was used to digitize features that were easily identified in the point cloud and Paracosm’s Retrace was used to digitize features for which imagery was needed for efficient identification. We developed several tools in Python to facilitate the process. We focused on classifying 29 features-of-interest to public safety personnel including walls, doors, windows, fire alarms, smoke detectors, and sprinklers.

The method we developed was efficient, accurate, and allowed successful mapping of features as small as a sprinkler head. Point cloud classification for a 14,000 m2 building took 20–40 hours, depending on building characteristics. Although the method is based on manual digitization, it provides a practical solution for indoor mapping using LiDAR. The methods can be applied in mapping a wide variety of features in indoor or outdoor environments.