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Among emerging 3D scanning and imaging techniques that are commercially available, simultaneous localization and mapping (SLAM) is being substantially studied to generate 2D/3D maps of an unknown environment while reliably keeping track of the user’s pinpoint locations. Its ubiquitous mobility has demonstrated great mapping capabilities for infrastructures where vertical information may frequently be occluded using unmanned aircraft system (UAS) structure from-motion (SfM) photogrammetry. In addition, indoor mapping with terrestrial laser scanning can be a cumbersome task due to possible multiple scan locations. Intending to provide a cohesive 3D model by fusing point clouds collected via aerial SfM photogrammetry, terrestrial laser scanning (TLS), and SLAM, the purpose of this work is to assess the performance of SLAM point cloud generated by a proprietary mobile backpack laser scanner (BLS). Considering maximum scanning range and information integration strategy as variables, the point clouds generated by the BLS were evaluated against SfM and TLS datasets in terms of the internal consistency as well as external accuracy. TLS, SfM and SAM data collection efforts were made in a typical university campus environment. For the internal consistency, the SLAM-based point cloud with a maximum scanning range of 70 m presented a root mean square error (RMSE) of 2 mm. The SLAM+GNSS-based point cloud presented the lowest internal precision of RMSE = 0.861 m. The SLAM+GNSS 70 point cloud after a fine adjustment of misalignment presented the highest vertical accuracy with an RMSE = 0.069 m, while the point cloud generated from SfM photogrammetry presented RMSE = 0.297 m. The BLS was able to generate point cloud with an accuracy similar to GNSS-RTK surveying and it can be considered as a viable solution for indoor and outdoor mapping applications.
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