Geometric calibration of a terrestrial laser scanner with local additional parameters: An automatic strategy

García-San-Miguel, D.; Lerma, José Luis

doi:10.1016/j.isprsjprs.2013.02.007

Cited by 28 publications

(22 citation statements)

References 9 publications

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“…In applications that require 3D reconstruction and/or geometric modelling (e.g., infrastructure documentation) the fit between the observations and the model can be improved. In addition, the overall processing time may be decreased due to reduced necessity of point cloud filtering/smoothing [6]. For deformation monitoring it can even lead to higher detection sensitivity.…”

Section: Discussionmentioning

confidence: 99%

“…It can also be executed efficiently once the point clouds are captured; for example the point targets can be extracted and matched automatically (e.g., Leica Cyclone) and the selection of the relevant error model parameters can be made with minimal user interaction [6]. However, its largest drawbacks are the time and cost required to set up a large quantity of signalised targets, and the poor recovery of the horizontal collimation axis error in hybrid-type scanners.…”

Section: Introductionmentioning

confidence: 99%

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Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods

Chow

Lichti

Glennie

et al. 2013

Sensors

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Terrestrial laser scanners are sophisticated instruments that operate much like high-speed total stations. It has previously been shown that unmodelled systematic errors can exist in modern terrestrial laser scanners that deteriorate their geometric measurement precision and accuracy. Typically, signalised targets are used in point-based self-calibrations to identify and model the systematic errors. Although this method has proven its effectiveness, a large quantity of signalised targets is required and is therefore labour-intensive and limits its practicality. In recent years, feature-based self-calibration of aerial, mobile terrestrial, and static terrestrial laser scanning systems has been demonstrated. In this paper, the commonalities and differences between point-based and plane-based self-calibration (in terms of model identification and parameter correlation) are explored. The results of this research indicate that much of the knowledge from point-based self-calibration can be directly transferred to plane-based calibration and that the two calibration approaches are nearly equivalent. New network configurations, such as the inclusion of tilted scans, were also studied and prove to be an effective means for strengthening the self-calibration solution, and improved recoverability of the horizontal collimation axis error for hybrid scanners, which has always posed a challenge in the past.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods

Chow

Lichti

Glennie

et al. 2013

Sensors

View full text Add to dashboard Cite

show abstract

“…To develop this task we had take into account the geometry corrections having to be with the camera/sensor and the pixel size or Ground Simple Distance (GSD). To calculate the aforementioned, we divided focal distance by flight distance, in respect to ground elevation (García & Lerma, 2013). The average size of GSD for the project is then eight centimeters.…”

Section: Phase I -Photogrammetric Processmentioning

confidence: 99%

Automatic Generation of Building Mapping Using Digital, Vertical and Aerial High Resolution Photographs and Lidar Point Clouds

Barragán

Campos

Sanchez

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VII, WG VII/4KEY WORDS: Cartography, Automatic Extraction, Photogrammetry, Aerial Photographs. Image Segmentation ABSTRACT:The objective of this research is automatic generation of buildings in the interest areas. This research was developed by using high resolution vertical aerial photographs and the LIDAR point cloud through radiometric and geometric digital processes. The research methodology usesknown building heights and various segmentation algorithms and spectral band combination. The overall effectiveness of the algorithm is 97.2% with the test data.

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“…A comparative quality index analysis will be carried out to select the best self-calibration adjustment under different parameterizations (García-San-Miguel and Lerma, 2013). The analysis follows four quality estimators: 1) for the observables; 2) for the EOP; 3) for the reference network; and 4) the sum of the three previous.…”

Section: Selection Of the Best Set Of Additional Parametersmentioning

confidence: 99%

Self-calibration of terrestrial laser scanners: selection of the best geometric additional parameters

Lerma

García-San-Miguel

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Systematic errors are present in laser scanning system observations due to manufacturer imperfections, wearing over time, vibrations, changing environmental conditions and, last but not least, involuntary hits. To achieve maximum quality and rigorous measurements from terrestrial laser scanners, a least squares estimation of additional calibration parameters can be used to model the a priori unknown systematic errors and therefore improve output observations. The selection of the right set of additional parameters is not trivial and requires laborious statistical analysis. Based on this requirement, this article presents an approach to determine the best set of additional parameters which provides the best mathematical solution based on a dimensionless quality index. The best set of additional parameters is the one which provides the maximum quality index (i.e. minimum value) for the group of observables, exterior orientation parameters and reference points. Calibration performance is tested using both a phase shift continuous wave scanner, FARO PHOTON 880, and a pulse-based time-of-flight system, Leica HDS3000. The improvement achieved after the geometric calibration is 30% for the former and 70% for the latter.

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Geometric calibration of a terrestrial laser scanner with local additional parameters: An automatic strategy

Cited by 28 publications

References 9 publications

Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods

Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods

Automatic Generation of Building Mapping Using Digital, Vertical and Aerial High Resolution Photographs and Lidar Point Clouds

Self-calibration of terrestrial laser scanners: selection of the best geometric additional parameters

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