Calibration of a Terrestrial Laser Scanner - Institute of Geodesy and ...

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82 4. Static Laser Scanning• There is no object point that can be acquired exactly and identicallya second time from a differentscanner position.• Blunders and noise of the point clouds have to be completely eliminated beforehand since they di¬rectly influence the registration algorithm.• The registration only refers to a local coordinate frame defined by one scanner position.The point cloud registration tries to find corresponding points within the point clouds despite each singlepoint not being acquired a second time. First,the scanning principle does not support the alignment ofthe laser beam exactly in the same direction a second time, due to the rough angle increment. Second, thechange of the viewpoint implies a change in the alignmentof the laser beam, which results in a differentposition of the footprint of the laser beam and a different angle of incidence. The quality of the point cloud isinfluenced by several parameters, e.g.and blunders.range, surface properties and angle of incidence, which cause noiseSince the point cloud registration directly uses the acquired data, the results are directlydependent on the quality of the point cloud. It is recommended to pre-process data with appropriate noisereduction and filter techniques. Since the point cloud registration algorithm works only with the acquiredpoint cloud, the reference frame is defined by one local reference frame. If the point cloud has to be includedin a global reference frame, additional control points with global coordinates are required.In recent years, several algorithms have been developed and are currently the topic of research projects.The most popular algorithm is the iterative-closest-points algorithm (ICP) developed by [Besl and McKay,1992], [Chen and Medioni, 1991], andfZhang, 1994]. This algorithm starts with two pointclouds and an es¬timate of aligning the rigid body transform. It then iteratively refines the transformation by alternating thesteps of choosing corresponding points across the point clouds, and finding the best rotation and translationthat minimizes an error metric based on the distance between corresponding points. The ICP algorithm isvaried and improved many times [Bergevin et al., 1996], [Masuda and Yokoya, 1996]. A detailed overviewat ICP algorithms can be found in [Guhring, 2002] and [Grun and Akca, 2005], who also introduced a newalgorithm, the least squares 3D surface matching.4.3 Modeling and VisualizationModeling is the step followed by registration. The objects described by a point cloud have to be processedin such away that geometrical information can be derived. The pointcloud is well-suited for onlinevisualization.All conceivable information are hidden within the point cloud. Although, point cloudscan be the final products of laser scanning, information derived from the point cloud are often what isrequired. Such information are frequently of geometrical nature, but classifications analogouslaser scanning and remote sensing are also conceivable to distinguish between different objectssurface, e.g. wet areas, roads, vegetation.to airborneon theThe processing of the point cloud depends on the project and the application. Based on these aspects themost suitable software has to be selected.Commercial software packages can be distinguished betweensoftware directly using the large number of points of the acquired point cloud and software operating onlywith a selected number of points.Working group V/3 from the International Society of Photogrammetry and Remote Sensing (ISPRS) haspublished the most popular software based on a questionnairein 2005. Table 4.1 summarizes the mostpopular software. The goal of using this software is of further interest, e.g. for registration, modeling,editing, visualization, data inspection,working group V/3.format conversion. More information can be found from the ISPRS
m\m)rdr4.3 Modeling and Visualization 83Table 4.1: Software for processing laser scanning data. The results are based on a questionnaire carried out by theISPRS working group V/3.Software Manufacturer Use [%]Cyclone HDS Leica Geosystems 28Geomagic Raindrop 17RiScanPRO Riegl 11CloudWorx HDS Leica Geosystems 9Polyworks InnovMetric 8rest various 274.3.1 Geometrical PrimitivesThe modeling of geometrical primitives is extensively automated. The parameters for geometrical objectsare computed by a least square adjustment using Gauss-Markov or Gauss-Helmert. The following objectsare considered as geometrical primitives and can be mostly computed semi-automatically or fully automat¬ically:• patch or plane: pin—= 0• sphere: \pt——= 0• cylinder : a(pl——= 0The vector n describes the unit normal, p defines each point of the point cloud, m is the center point of thesphere or the cylinder, and a characterizes the orientation of the principal axis of the cylinder.The para¬meters r and d define the radius and the diameter, respectively. A more detailed description for calculatinggeometrical primitives can be found in [Hovenbitzer, 2003].4.3.2 TriangulationTriangulation or mesh generation can be described as the process of finding geometrical objects in a givendata set, i.e.triangles in 2D and tetrahedra in 3D. Typically, this process is based on Delauny triangulation ormarching cubes [Hoppe et al., 1992] and [Carr et al., 2001]. Some possible constraints for building trianglesor tetrahedra are defined by [Hoscheck and Lasser, 1992]• criterion of the shortest diagonal,• criterions of the minimum/maximum angle and maximum/minimum angle, respectively, and• criterion of the circumscribed circle.The last criterion leads to the Delauny triangulation, which postulatesthat the circumscribed circle of eachtriangle must not contain a fourth point of the data set [deBerg et al., 2002]. The Delauny triangulationcomputes uniform triangles and avoids both long and short triangles as well as overlapping triangles.More information can be found in a number of publications, e.g. [deBerg et al., 2002], [Carr et al., 2001][Hoppe et al., 1992], [Hoscheck and Lasser, 1992], [Rietdorf, 2005].
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DISS. ETH NO. 17036Calibration of a
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ZusammenfassungSeit einigen Jahren
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ContentsAbstractiiiZusammenfassungv
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Contentsix6.1.3 Results Ill6.2 Stat
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1Introduction1.1 Terrestrial Laser
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1.2 Motivation 3Precision [m10~1 "1
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6 1. Introduction
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8 2. Components of Terrestrial Lase
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10 2. Components of Terrestrial Las
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24 3. Calibration of Terrestrial La
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'28 3. Calibration of Terrestrial L
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132 7. Summaryrange, achievable by
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134 7. Summary
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136 A. Imaging System of Imager 500
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138 B. Technical Data of Imager 500
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u140 C. Adjustment of SphereA =dfi,
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142 C. Adjustment of Sphere
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144 D. Electronic Circuit for Deter
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aMulti-SensorPlatform,PhDGeodesy Sw
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Accuracy-AeinTPS1200 AG, -Version 1
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-GenauigkeitsbetrachtungenInvestiga
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152 BibliographyWunderlich, T. A. [
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List of Figures3.22 Residuals of th
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156 List of Figures
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158 List of Tables
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200220072002Curriculum VitaePersona
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