Abstract
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.
Highlights
1.1 IntroductionMaps of building interiors provide critical information that allows public safety personnel to pre-plan responses to disasters, building managers to efficiently manage their facilities, and visitors to find their destinations
The recent emergence of handheld Light detection and ranging (LiDAR) scanners provides a practical solution to indoor mapping by combining LiDAR with high mobility
These scanners use inertial mapping units (IMU) with simultaneous localization and mapping (SLAM) algorithms (Droeschel and Behnk 2018; Diosi and Kleeman 2005; Thrun et al 2004; Castellanos et al 1998) to generate point cloud data as the unit is carried through the building
Summary
1.1 IntroductionMaps of building interiors provide critical information that allows public safety personnel to pre-plan responses to disasters, building managers to efficiently manage their facilities, and visitors to find their destinations. The recent emergence of handheld LiDAR scanners provides a practical solution to indoor mapping by combining LiDAR with high mobility. These scanners use inertial mapping units (IMU) with simultaneous localization and mapping (SLAM) algorithms (Droeschel and Behnk 2018; Diosi and Kleeman 2005; Thrun et al 2004; Castellanos et al 1998) to generate point cloud data as the unit is carried through the building. These scanners typically integrate a wide angle or spherical RGB camera to collect images simultaneously with the LiDAR data and which can be used to colorize the point cloud
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