This study evaluates the quality of point clouds generated by Simultaneous Localization and Mapping (SLAM) through empirical tests in a laboratory and a large church with complex geometries. SLAM-generated point clouds were compared with those from Terrestrial Laser Scanners (TLS) using CloudCompare software, analyzing noise parameters like C2C distance, standard deviation, and Root Mean Square Error (RMSE). Statistical analysis and filtering techniques ensured accuracy assessment. Results showed that SLAM effectively reconstructed simple geometries but had gaps in complex architectural details. Increasing scan iterations improved accuracy, with two scans providing an optimal balance. Noise was most pronounced on the ceiling and floor but was effectively reduced by the Statistical Outlier Removal (SOR) filter, though multiple filtering iterations had diminishing returns. Comparative analysis confirmed SLAM’s reliability in structured environments, though sensor upgrades are necessary for capturing intricate surfaces. Overall, while SLAM is effective for simple settings, improvements are needed for more complex reconstructions.
The Evaluation of the Quality of the SLAM Data for the 3D Reconstruction of Architecture and Construction
Dewedar, Ahmed Kamal Hamed;Pepe, Massimiliano
2025-01-01
Abstract
This study evaluates the quality of point clouds generated by Simultaneous Localization and Mapping (SLAM) through empirical tests in a laboratory and a large church with complex geometries. SLAM-generated point clouds were compared with those from Terrestrial Laser Scanners (TLS) using CloudCompare software, analyzing noise parameters like C2C distance, standard deviation, and Root Mean Square Error (RMSE). Statistical analysis and filtering techniques ensured accuracy assessment. Results showed that SLAM effectively reconstructed simple geometries but had gaps in complex architectural details. Increasing scan iterations improved accuracy, with two scans providing an optimal balance. Noise was most pronounced on the ceiling and floor but was effectively reduced by the Statistical Outlier Removal (SOR) filter, though multiple filtering iterations had diminishing returns. Comparative analysis confirmed SLAM’s reliability in structured environments, though sensor upgrades are necessary for capturing intricate surfaces. Overall, while SLAM is effective for simple settings, improvements are needed for more complex reconstructions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
