Effect of target color and scanning geometry on terrestrial LiDAR point-cloud noise and plane fitting

Abstract

Point-cloud coordinate information derived from terrestrial Light Detection And Ranging (LiDAR) is important for several applications in surveying and civil engineering. Plane fitting and segmentation of target-surfaces is an important step in several applications such as in the monitoring of structures. Reliable parametric modeling and segmentation relies on the underlying quality of the point-cloud. Therefore, understanding how point-cloud errors affect fitting of planes and segmentation is important. Point-cloud intensity, which accompanies the point-cloud data, often goes hand-in-hand with point-cloud noise. This study uses industrial particle boards painted with eight different colors (black, white, grey, red, green, blue, brown, and yellow) and two different sheens (flat and semi-gloss) to explore how noise and plane residuals vary with scanning geometry (i.e., distance and incidence angle) and target-color. Results show that darker colors, such as black and brown, can produce point clouds that are several times noisier than bright targets, such as white. In addition, semi-gloss targets manage to reduce noise in dark targets by about 2–3 times. The study of plane residuals with scanning geometry reveals that, in many of the cases tested, residuals decrease with increasing incidence angles, which can assist in understanding the distribution of plane residuals in a dataset. Finally, a scheme is developed to derive survey guidelines based on the data collected in this experiment. Three examples demonstrate that users should consider instrument specification, required precision of plane residuals, required point-spacing, target-color, and target-sheen, when selecting scanning locations. Outcomes of this study can aid users to select appropriate instrumentation and improve planning of terrestrial LiDAR data-acquisition.

Methods From Information Extraction From Lidar Intensity Data and Multispectral Lidar Technology

“…It was shown that the impact of interest increases for rising incidence angles. Interestingly, a stringent explanatory pattern can still be found in current research, see, e.g., Ozendi et al (2017) and Bolkas & Martinez (2018), that assign various influences into certain categories. In summary, the most relevant influences can be tributed to the survey configuration as well as the local radiometric characteristics of the object.…”

Beam Deflection Monitoring Based on a Genetic Algorithm Using Lidar Data

“…The scanner device stands 2.5 m away from the front web face while considering which factors affect the accuracy of the data. Target color, incidence angle, range and intensity are the main factors that affect the point cloud noise and parametric model fitting [13]. Consequently, this setup considers all the results from Bolkas et al in accordance with the available space in the laboratory •…”

“…Similar to the loading scenario, the fitness function for the loading case is obtained using Equation (13). A 2nd degree of polynomial curve is constructed for the fitness function considering the complexity degree of the structure based on given experimental study.…”

“…Among them, random errors due to inherent physical properties are often difficult to eliminate while error due to environmental and scanning geometry issues can be removed through analysis [12]. Consequently, attention should be given to data acquisition by considering the position, incidence angle, and color of a specimen [13]. As a result, it is possible to dwindle noises tremendously.…”

“…Naturally, Lidar data are highly vulnerable and easily affected by noise [26]. Different scholars have been studying the factors which affect the Lidar data regarding structural health monitoring [13,27]. On the other hand, some researchers have used different types of denoising methods and optimization techniques in order to diminish their effect [28,29].…”