Assessment the Contribution of the Major Quality Influencing Factors on the Measurements of TLS Scan

Surface qualities, range detectors, and measuring travel time are all major quality-influencing factors that affect the position accuracy of the measured point clouds by the terrestrial laser scanner (TLS). We conducted experimental tests using ScanStation2 TLS to investigate the accuracy of the scanned point clouds at various incident angles and ranges, and then examined the influence of different scanned surfaces on roughness and reflectivity. In this study, we selected four distinct surface properties from various materials (glass, steel, wood, ekoplast, and adhesive total station (TS) target), and painted three of these materials in five different colors to investigate the influence of colored surfaces on the reflected measured point clouds. During the experiment, approximately 250 scans were recorded, as the chosen materials were scanned at six scan angles (0, 15, 30, 45, 60, and 75) and ranges of 5m, 20m, 40m, and 60m. The experiment's findings show that, at various incident angles, smooth surfaces have a greater impact on the accuracy of the measured 3D points than do rough surfaces. Conversely, the total RMSEs of the red and black colors were greater than those of the blue, green, and white colors. At 0˚ incident angle, the TS target reflects approximately 20 cm closer to the TLS than the other materials; this difference decreases as the scan angle increases. In comparison to the other materials, the difference becomes about 2 mm at a 75˚ incidence angle. With the exception of the 30˚ scan angle of wood material, the maximum RMSE of rough materials is less than 1 cm, while the highest RMSE for smooth surfaces at 45˚ glass material is 4 cm. Moreover, the intensity of different materials varies significantly. For example, smooth materials like steel and glass have varying degrees of accuracy because of their respective properties. We have created a best-fit patch for all the scanned points to detect their deviation and characterize a suitable correction method. Due to the huge number of point clouds that resulted from those hundreds of scans at different conditions, it is very difficult and complex to directly apply a point position correction for all those complicated scanning conditions. Therefore, in this study, a comprehensive and intensive Python programming code was developed to correct a large number of point cloud positions within a standard processing time. This, in turn, contributes to the process's time and cost savings. Interestingly, this developed code is a novel procedure for correcting TLS point clouds at different measurement conditions, so it will be a good suggestion to add it to the Cyclone software.