High precision target center determination from a point cloud

Kregar, Klemen; Grigillo, Dejan; Kogoj, Dušan

doi:10.5194/isprsannals-ii-5-w2-139-2013

Cited by 10 publications

(10 citation statements)

References 7 publications

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“…Algorithm: From the point cloud recorded by the laser scanner, the coordinates of the target are estimated by means of an algorithm. For this, there are multiple algorithmic approaches [26][27][28]. It is assumed that not all algorithms provide equally precise coordinates.…”

Section: Influencing Factors On the Precision Of Target Center Estimamentioning

confidence: 99%

Decreasing the Uncertainty of the Target Center Estimation at Terrestrial Laser Scanning by Choosing the Best Algorithm and by Improving the Target Design

et al. 2019

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During the registration and georeferencing of terrestrial laser scans, it is common to use targets to mark discrete points. To improve the accuracy of the registration, the uncertainties of the target center estimation (TCE) have to be minimized. The present study examines different factors influencing the precision of the TCE. Here, the focus is on the algorithm and the target design. It is determined that, in general, the uncertainties of the TCE are much smaller than those indicated by the manufacturers. By comparing different algorithms for the first time, it was possible to clearly determine that an algorithm using image correlations yields the smallest standard deviations for the TCE. A comparison of different target designs could not identify an ideal commercially available target. For this reason, a new target, the BOTA8 (BOnn TArget with 8-fold pattern) was developed, which leads to smaller standard deviations than the previous targets. By choosing the best algorithm and improving the target design, standard deviations of 0.5 mm in distance direction and 1.2 arcsec in angular direction for a scan distance up to 100 m were achieved with the laser scanner Leica ScanStation P20. The uncertainties could be reduced by several millimetres and angular seconds compared to the manufacturer’s targets and software.

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Section: Influencing Factors On the Precision Of Target Center Estimamentioning

confidence: 99%

Decreasing the Uncertainty of the Target Center Estimation at Terrestrial Laser Scanning by Choosing the Best Algorithm and by Improving the Target Design

et al. 2019

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“…The ATOS target centers were processed simultaneously with data acquisition in the ATOS I software. TLS target centers were computed using an image matching algorithm (Kregar et al 2013). Using target coordinates the TLS and ATOS datasets are co-registered and transformed into a new coordinate system aligned with the joint plane.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Quantification of Rock Joint Roughness Using Terrestrial Laser Scanning

Bitenc

Kieffer

Khoshelham

et al. 2014

Engineering Geology for Society and Territory - Volume 6

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Rock joint roughness characterization is often an important aspect of rock engineering projects. Various methods have been developed to describe the topography of the joint surface, for example Joint Roughness Coefficient (JRC) correlation charts or disc-clinometer measurements. The goal of this research is to evaluate the accuracy, precision and limits of Terrestrial Laser Scanning (TLS) for making remote measurements of large-scale rock joints. In order to find the most appropriate roughness parameterization method for TLS data and to analyse the capability of TLS for roughness estimation, experiments were made with a 20 × 30 cm joint sample. The sample was scanned with TLS and compared to reference measurements made with the Advanced TOpometric Sensor (ATOS) system. Analysis of two roughness parameterization methods, virtual compass and disc-clinometer, and angular threshold method, showed that the latter is less sensitive to noise. Comparative studies of ATOS and TLS roughness parameters indicate that the TLS can adequately quantify surface irregularities with a wavelength greater than 5 mm from a distance of 10 m.

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“…The black-white targets included on the sample mounting board were needed to coregister the TLS and ATOS data in postprocessing. Highprecision TLS target centers were measured in the point cloud by applying an algorithm based on image matching (Kregar et al 2013) and ATOS target centers were identified automatically with built-in software.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Medians and robust standard deviations (robust STD) of height differences for the 11 scanning configurations are presented in Table 3. Negative median height differences indicate systematic co-registration errors, which result from uncertainties of TLS target centre estimation (Kregar et al 2013), but do not influence surface roughness values. The robust STD of dZ noisy values is an indicator of TLS noise level, and attenuates after range denoising is performed.…”

Section: Rock Surface Representationsmentioning

confidence: 99%

Range Versus Surface Denoising of Terrestrial Laser Scanning Data for Rock Discontinuity Roughness Estimation

2019

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Surface roughness represents a major component of rock discontinuity shear strength. To achieve comprehensive, accurate, and efficient estimates of in situ discontinuity roughness, the traditional contact measuring methods are being replaced by advanced remote-sensing technologies. Terrestrial laser scanner (TLS) is well suited for measuring large inaccessible discontinuities; however, inherent TLS range noise strongly influences the surface details and roughness estimation. The aim of this research is to establish an optimal wavelet-denoising procedure for the TLS data acquired with different scanning configurations (range and incidence angle), and for rock discontinuities having different roughness characteristics and surface reflectivity. The conventional discrete wavelet transform and stationary wavelet transform in combination with four threshold selection methods are applied on TLS data in the direction of range measurements (range denoising) and in the direction perpendicular to the best-fit plane (surface denoising). The performance of the denoising procedures is assessed by comparing the range and surface-denoised TLS surfaces with reference surfaces acquired with the Advanced TOpometric Sensor. Comparative analyses of the roughness calculated according to the angular thresholding method (Grasselli, in Shear strength of rock joints based on quantified surface description, Ph.D. thesis. EPF Lausanne, Lausanne; Grasselli, Shear strength of rock joints based on quantified surface description, Ph.D. thesis, EPF Lausanne, Lausanne, 2001) indicate that all the denoising methods improve the roughness estimated from the TLS data appreciably; however, the level of improvement depends intrinsically on geometrical characteristics of the rock surface and scanning configuration. Range denoising has been found to provide more reliable noise estimations.

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High precision target center determination from a point cloud

Cited by 10 publications

References 7 publications

Decreasing the Uncertainty of the Target Center Estimation at Terrestrial Laser Scanning by Choosing the Best Algorithm and by Improving the Target Design

Decreasing the Uncertainty of the Target Center Estimation at Terrestrial Laser Scanning by Choosing the Best Algorithm and by Improving the Target Design

Quantification of Rock Joint Roughness Using Terrestrial Laser Scanning

Range Versus Surface Denoising of Terrestrial Laser Scanning Data for Rock Discontinuity Roughness Estimation

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