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

  • Dimitrios Bolkas EMAIL logo and Aaron Martinez
Published/Copyright: December 20, 2017
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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.

Acknowledgment

This research study was supported and funded by the Scholarly Activities Committee of the Pennsylvania State University, Wilkes-Barre Campus. Also, special thanks go to Mr. Frank Lenik from Leica Geosystems for providing the Leica Scanstation P40 and Cyclone software. Finally, we would like to thank the two anonymous reviewers for their valuable comments and suggestions that improved this manuscript.

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Received: 2017-9-10
Accepted: 2017-11-28
Published Online: 2017-12-20
Published in Print: 2018-1-26

© 2018 Walter de Gruyter GmbH, Berlin/Boston

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