Assessment of Terrestrial LiDAR Point-Cloud Noise for Deformation Monitoring of Structures
Terrestrial Light Detection And Ranging (LiDAR) is often used in surveying and civil engineering applications of deformation monitoring of structures. Plane estimation is often an important step for deformation monitoring of flat structures and surfaces, and segmentation. Reliable estimation of planes relies on the underlying quality of the point-cloud; therefore, understanding how point-cloud errors affect plane estimation is important. Parameters that affect plane estimation from terrestrial LiDAR point-clouds are target characteristics (e.g., reflectance and roughness) and scanning geometry (distance and incidence angle). To assess how target-intensity affects point-cloud noise, this study utilizes targets painted with eight different colors (black, white, grey, red, green, blue, brown, and yellow) and two different sheens (flat and semi-gloss). These targets are scanned at distances from 3 m to 90 m and incidence angles from 0° to 85°. Two laser scanners are tested, namely, the Leica Scanstation P40 and the Topcon GLS-1500. Results of target-intensity show that darker colors, such as brown and black, produce noisier point-clouds than bright ones. In addition, semi-gloss targets manage to reduce noise in dark targets by ~2-3 times. Furthermore, the study of plane-residuals and data-density with scanning geometry can assist in understanding the spatial distribution of plane residuals and aid users to improve planning of terrestrial laser scanning data-acquisition.