Abstract
An efficient 3D survey of a complex indoor environment remains a challenging task, especially if the accuracy requirements for the geometric data are high for instance in building information modeling (BIM) or construction. The registration of non-overlapping terrestrial laser scanning (TLS) point clouds is laborious. We propose a novel indoor mapping strategy that uses a simultaneous localization and mapping (SLAM) laser scanner (LS) to support the building-scale registration of non-overlapping TLS point clouds in order to reconstruct comprehensive building floor/3D maps. This strategy improves efficiency since it allows georeferenced TLS data to only be collected from those parts of the building that require such accuracy. The rest of the building is measured with SLAM LS accuracy. Based on the results of the case study, the introduced method can locate non-overlapping TLS point clouds with an accuracy of 18–51 mm using target sphere comparison.
Highlights
The 3D mapping of indoor environments is beneficial for many applications, such as the documentation of constructions or historical buildings (e.g., [1,2]), building diagnostics (e.g., [3]), the life cycle of buildings (e.g., [4]), and building information modeling (BIM) (e.g., [5,6,7])
The errors include the errors of individual target spheres as well as the root mean square errors (RMSEs) between the reference point cloud and the simultaneous localization and mapping (SLAM) laser scanner (LS) point cloud based on the target spheres that are located outside the building
Our experiment showed that the non-overlapping terrestrial laser scanning (TLS) point clouds of the rooms can be located with SLAM support (TLSroom) to an accuracy of 39.96 mm for a mapping path that covers the whole room and 34.94 mm for the mapping path that only briefly visits the room in the proximity of the door in the target sphere comparison. (Table 6)
Summary
The 3D mapping of indoor environments is beneficial for many applications, such as the documentation of constructions or historical buildings (e.g., [1,2]), building diagnostics (e.g., [3]), the life cycle of buildings (e.g., [4]), and building information modeling (BIM) (e.g., [5,6,7]). There are many methods with which to collect data from indoor environments, such as terrestrial laser scanning (TLS) (e.g., [8,9]), photogrammetry (e.g., [10,11]), using depth cameras (e.g., [12,13]), and using simultaneous localization and mapping (SLAM) laser scanners (LSs) (e.g., [14,15]). Though TLS is accurate, it is expensive and the most limiting factor is its static data collection principle, which only provides a limited number of observation points in the capturing of often polymorphic structures [3,16]
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