Rock surface roughness estimation using TLS data

Maja Bitenc

Research output: Contribution to conferenceAbstractResearch

Abstract

Rock surface roughness is an important property influencing the shear strength of a rock mass and
therefore its stability. For safety reasons and appropriate engineering design on potentially instable
slopes, accurate and reliable measurement of surface roughness is needed. In this research the stateof-the-art terrestrial laser scanning (TLS) technology is used to remotely sense large rock surfaces
and further on estimate the roughness as a function of direction and scale. The research attempts to
answer the following question: what is the smallest scale of surface roughness that can be reliably
extracted from TLS point clouds? To answer the question, TLS capabilities and limitations are studied
in detail. The main limitation of TLS is the range measurement noise, which can result in
overestimation of surface roughness. To reduce the noise effect, different image de-noising methods
(Discrete Wavelet Transform and Non-Local Mean) are applied on TLS meshes and their results are
compared. A second limitation of TLS data is the effective resolution of the point cloud caused by the
divergence of laser beam, which defines the smallest observable surface detail. Therefore, the
effective resolution of TLS data is analyzed. Firstly a theoretical-empirical method based on Average
Modulated Transfer Function is applied. This method implies some assumptions about measurement
procedure in a laser scanner that usually cannot be tested or are unknown to the end-user. Thus, we
aim to develop an empirical method that is time-efficient and simple in order to be performed in-situ
immediately before or after rock surface acquisition.
Original languageEnglish
Publication statusPublished - 26 Apr 2017

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surface roughness
laser
rock
scanner
transfer function
art
shear strength
wavelet
roughness
transform
safety
engineering
method

Keywords

  • rock surface roughness
  • TLS
  • noise
  • EIFOV

Cite this

Rock surface roughness estimation using TLS data. / Bitenc, Maja.

2017.

Research output: Contribution to conferenceAbstractResearch

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title = "Rock surface roughness estimation using TLS data",
abstract = "Rock surface roughness is an important property influencing the shear strength of a rock mass andtherefore its stability. For safety reasons and appropriate engineering design on potentially instableslopes, accurate and reliable measurement of surface roughness is needed. In this research the stateof-the-art terrestrial laser scanning (TLS) technology is used to remotely sense large rock surfacesand further on estimate the roughness as a function of direction and scale. The research attempts toanswer the following question: what is the smallest scale of surface roughness that can be reliablyextracted from TLS point clouds? To answer the question, TLS capabilities and limitations are studiedin detail. The main limitation of TLS is the range measurement noise, which can result inoverestimation of surface roughness. To reduce the noise effect, different image de-noising methods(Discrete Wavelet Transform and Non-Local Mean) are applied on TLS meshes and their results arecompared. A second limitation of TLS data is the effective resolution of the point cloud caused by thedivergence of laser beam, which defines the smallest observable surface detail. Therefore, theeffective resolution of TLS data is analyzed. Firstly a theoretical-empirical method based on AverageModulated Transfer Function is applied. This method implies some assumptions about measurementprocedure in a laser scanner that usually cannot be tested or are unknown to the end-user. Thus, weaim to develop an empirical method that is time-efficient and simple in order to be performed in-situimmediately before or after rock surface acquisition.",
keywords = "rock surface roughness, TLS, noise , EIFOV",
author = "Maja Bitenc",
year = "2017",
month = "4",
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N2 - Rock surface roughness is an important property influencing the shear strength of a rock mass andtherefore its stability. For safety reasons and appropriate engineering design on potentially instableslopes, accurate and reliable measurement of surface roughness is needed. In this research the stateof-the-art terrestrial laser scanning (TLS) technology is used to remotely sense large rock surfacesand further on estimate the roughness as a function of direction and scale. The research attempts toanswer the following question: what is the smallest scale of surface roughness that can be reliablyextracted from TLS point clouds? To answer the question, TLS capabilities and limitations are studiedin detail. The main limitation of TLS is the range measurement noise, which can result inoverestimation of surface roughness. To reduce the noise effect, different image de-noising methods(Discrete Wavelet Transform and Non-Local Mean) are applied on TLS meshes and their results arecompared. A second limitation of TLS data is the effective resolution of the point cloud caused by thedivergence of laser beam, which defines the smallest observable surface detail. Therefore, theeffective resolution of TLS data is analyzed. Firstly a theoretical-empirical method based on AverageModulated Transfer Function is applied. This method implies some assumptions about measurementprocedure in a laser scanner that usually cannot be tested or are unknown to the end-user. Thus, weaim to develop an empirical method that is time-efficient and simple in order to be performed in-situimmediately before or after rock surface acquisition.

AB - Rock surface roughness is an important property influencing the shear strength of a rock mass andtherefore its stability. For safety reasons and appropriate engineering design on potentially instableslopes, accurate and reliable measurement of surface roughness is needed. In this research the stateof-the-art terrestrial laser scanning (TLS) technology is used to remotely sense large rock surfacesand further on estimate the roughness as a function of direction and scale. The research attempts toanswer the following question: what is the smallest scale of surface roughness that can be reliablyextracted from TLS point clouds? To answer the question, TLS capabilities and limitations are studiedin detail. The main limitation of TLS is the range measurement noise, which can result inoverestimation of surface roughness. To reduce the noise effect, different image de-noising methods(Discrete Wavelet Transform and Non-Local Mean) are applied on TLS meshes and their results arecompared. A second limitation of TLS data is the effective resolution of the point cloud caused by thedivergence of laser beam, which defines the smallest observable surface detail. Therefore, theeffective resolution of TLS data is analyzed. Firstly a theoretical-empirical method based on AverageModulated Transfer Function is applied. This method implies some assumptions about measurementprocedure in a laser scanner that usually cannot be tested or are unknown to the end-user. Thus, weaim to develop an empirical method that is time-efficient and simple in order to be performed in-situimmediately before or after rock surface acquisition.

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