Božić, Branko

Erie, V.

Pandžić, Jelena

Pejić, Marko

English

 GraFar - Repository of the Faculty of Civil Engineering

  101 
TS 4 – Poster presentations  
 
INGEO 2014 – 6th International Conference on Engineering Surveying 
Prague, Czech republic, April 3-4, 2014 
 
The Accuracy Analysis of Leica ScanStation P20 Data  
by Means of Point Cloud Fitting Algorithm 
 
 
Pandžić, J., Erić, V., Božić, B. and Pejić, M. 
 
University of Belgrade, Faculty of Civil Engineering, Department of Geodesy and 
Geoinformatics, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia,  
Web site: www.grf.bg.ac.rs,  
E-mail: jpandzic@grf.bg.ac.rs, veric@grf.bg.ac.rs, bozic@grf.bg.ac.rs, mpejic@grf.bg.ac.rs 
  
 
Abstract 
 
Terrestrial laser scanning (TLS), although still considered as an exotic method for data 
acquisition, was successfully introduced in the field of engineering geodesy more than a 
decade ago. Recent TLS improvements regarding its performance, range and accuracy have 
opened up new challenges within engineering. TLS is a method of acquiring huge amount of 
data in a relatively short time period and with acceptable accuracy. Therefore, one can often 
ask oneself for which engineering tasks this modern method of surveying can successfully 
take place of classical methods. Aim of this paper was to compare one aspect of quality of the 
data acquired by a state-of-the-art scanner, Leica ScanStation P20, and by total station (TS). 
This was achieved by analyzing characteristic planes obtained from acquired data using least 
squares fitting method. Test field was the building of the Faculty of Civil Engineering in 
Belgrade. Applied statistical tests showed no significant difference between the plane fitted to 
the point cloud data acquired by TLS and the one fitted to the data acquired by TS.  
 
Key words: Terrestrial laser scanning, ScanStation P20, Best-fit plane, Accuracy analysis 
 
1 INTRODUCTION 
Terrestrial laser scanning (TLS) is a contactless method for acquiring huge amount of data 
on terrain and various man-made constructions. It has been successfully employed in 
engineering geodesy for more than a decade now and is constantly developing. Still, just 
because it is a relatively new surveying method, TLS faces some obstacles regarding its use in 
engineering. When it comes to doing some “serious” work, most surveyors tend to rely on 
traditional surveying methods, i.e. employing a total station (TS) for data acquisition. This is 
mostly due to some unknowns regarding scanner quality performances. 
However, these days there have been more and more studies on application of TLS in 
engineering (Pejić et al., 2013; Kopáčik and Wunderlich, 2004) claiming satisfying quality of 
data acquired by a scanner. This was to some extent the aim of this paper as well: to compare 
the accuracy of the results obtained by the terrestrial laser scanner and the total station. 
Most frequently used methods of plane fitting are least squares fitting method (Hoppe et 
al., 1992; Jianfeng and Kazhong, 2013), Principal Component Analysis - PCA (Nurunnabi et 
al., 2012; Weingarten et al., 2004) and RANdom Sample Consensus - RANSAC (Schnabel et 
al., 2007; Zhang, 2012). 
102 INGEO 2014 
 
 
 
Hundreds of thousands or even millions of points forming so-called point cloud are not the 
ultimate goal of terrestrial laser scanning. Point cloud is just a stage in obtaining some final 
results, whether that would be a model of terrain or construction, characteristic planes or lines, 
sections or whatever can be used in subsequent analysis. Two construction planes were 
investigated within this paper, meaning that the comparison between planes obtained from 
TLS data on one hand and data acquired by a total station on the other hand was performed. 
Parameters of all of the aforementioned planes, as well as standard deviations of these 
parameters, were obtained by the least squares method. 
2 METHODOLOGY 
2.1 CONTROL NETWORK 
 
Test field for the experiment was the building of the Faculty of Civil Engineering in 
Belgrade. Firstly, the control network was established, consisting of seven reinforced concrete 
pillars already stabilized in the backyard of the Faculty building (this network has been used 
as an instrument test field on regular basis). Positions of the control network points (marked 
as 1 to 7, Fig. 1) in a local coordinate system were determined by employing separate 2D and 
1D adjustments in PANDA 4.20 software. Both adjustments were performed by minimizing 
the trace of variance-covariance matrix. Horizontal directions and distances were measured by 
using Sokkia SET3130R3 total station, while height differences were obtained by using 
Sokkia SDL30 digital level combined with fibreglass rods. Declared angular accuracy of the 
total station is 3", while its accuracy of distance measurement is 2 mm + 2 ppm. The accuracy 
of digital level is given as standard deviation of 1 mm/km when using fibreglass rods. Prior to 
fieldwork instruments were tested in laboratory. 
 
Figure 1 Control network 
 
The standard deviations of the estimated 2D point positions ranged from 0.3 mm (point 3) 
to 0.4 mm (point 1), while the standard deviations of the estimated point heights were in the 
range between 0.1 mm (points 3 and 4) and 0.2 mm (point 7). The control network itself is not 
a prerequisite for scanning, but since it had already been established it was later utilized in 
scanning and point cloud georeferencing. 
2.2 STRUCTURE SCANNING 
 
In the next step, the back part of the building was scanned. The scanning process was 
carried out by using state-of-the-art terrestrial laser scanner Leica ScanStation P20 (Fig. 2). 
The Leica’s latest pulsed TLS enables ultra-fast acquisition of high quality data with scan rate 
up to one million points per second and maximum range of 120 m. According to the 
specifications published by the manufacturer, its horizontal and vertical angular accuracy is 
 Pandžić, J. et al.: The accuracy analysis of Leica ScanStation P20 data by means of… 103 
8", while the linearity error is less than or equal to 1 mm 
(Leica, 2013). This is the first time Leica is using the term 
“linearity error” instead of “distance accuracy”. 
The scanning was performed from two locations: pillar 
5 and station p2 (Fig. 1) which required setting up the 
scanner on a tripod. Pillar 3 was not used as a station 
because of certain obstacles blocking a view towards the 
building. For the purpose of georeferencing square black 
and white (B&W) targets (14 cm x 14 cm) placed on 
pillars 1, 2, 3 and 4 were used in case of scanning from 
station 5, and on pillars 1 to 5 in case of scanning from station p2. Fine-resolution scanning 
was performed on all visible targets, while the building itself was scanned with the resolution 
of 6.3 mm at 10 m. The average distance between building and stations 5 and p2 was 30 m 
and 18 m, respectively. 
Processing of the data obtained during the scanning procedure was performed in Leica 
Cyclone 8.0 software. The authors endeavoured to implement the procedure of direct 
georeferencing in the field but unfortunately the attempt was unsuccessful. Even though the 
metadata on implemented georeferencing method (station-orientation for 5 and resection for 
p2) was visible in Cyclone, the software didn't utilize it. None of the scans were actually in 
the terrain coordinate system, but rather in a coordinate system of the scanner which 
ultimately required implementing indirect georeferencing. 
Indirect georeferencing was done in Cyclone by registering scans from stations 5 and p2 
using identical control network points (points 1-4). The mean absolute error of the performed 
registration was 2.1 mm. The resulting georeferenced point cloud contained a lot of points 
which represented objects of no interest in this particular case, so “cleaning” of the point 
cloud was required. Afterwards, this reduced georeferenced point cloud was unified, i.e. the 
point cloud was resampled with point spacing set to 5 cm. The final point cloud used in the 
further analysis consisted of a little more than 3 million points. 
 
2.3 PLANE FITTING AND ANALYSIS 
Data needed for comparing characteristic construction planes was obtained from the 
created model, as well as by using the total station. The two chosen construction surfaces cca 
6.7 m × 1.3 m in size (Fig. 3, surfaces marked with a red X sign) were discretized with 35 and 
30 points respectively, acquired by the laser total station centred over the control network 
point 5 and oriented towards the control network point 1. The idea was to pick surface points 
from the model which in a way correspond to the points collected by the total station. 
The X coordinates of the points-to-be were obtained from the model derived from the TLS 
data at the same Y and Z positions as the ones of the points surveyed by the total station. 
Thereby it was possible to have two point datasets (TS and TLS) differing only in X 
coordinates. The aforementioned X coordinates of the chosen points were obtained from the 
model created in the AutoCAD Civil 3D 2012 software package (the model was based on the 
previously resampled point cloud). Prior to obtaining the point coordinates from the model, all 
data was transformed. A simple rotation of the coordinate system about the Z-axis was 
performed by defining the Y-axis via two horizontally most distant points from the TS 
dataset. The intention was to bring the observed façade planes to be parallel to the YZ plane 
of the coordinate system. This should not be taken too formally since the mentioned 
parallelism cannot strictly be achieved in practice. For the purpose of facilitating further 
analysis the planes were assumed to be vertical. 
Figure 2 Terrestrial laser 
scanner Leica ScanStation P20 
104 INGEO 2014 
 
 
 
 
Figure 3 Characteristic construction planes 
 
Planes were fitted to the point data by the means of ordinary least squares method. The 
whole estimation procedure was conducted in the MATLAB software package. In accuracy 
analysis it had been presumed that Y and Z coordinates were error-free (only X coordinates 
were treated as measurements), thus giving the general plane equation in the form: 
 CZBYA X +⋅+⋅= . (1) 
Estimation of the plane parameters was obtained by minimizing the sum of squares of 
point-to-plane residuals by the X-axis. In addition to estimating plane parameters, their 
standard deviations were assessed as well. These parameters and standard deviations were 
further used in statistical testing. 
The first thing that was put on a test was the significance of the estimated plane 
parameters, i.e. the equality of these parameters and their corresponding assumed values. Both 
of the observed construction planes are assumed to be parallel to the YZ plane of the 
coordinate system, leaving the normal vector to the plane to be (1,0,0). This led to defining 
the null and the alternative hypothesis of the statistical test: 
 
0)Bˆ (and  0Aˆ:H
0)Bˆ (and  0Aˆ:H
a
0
≠≠
==
, (2) 
with Bˆ and Aˆ  being the estimated plane parameters. 
The value of the test statistic in this case was obtained through: 
 ⎟⎟⎠
⎞
⎜⎜⎝
⎛ −=−=
BA σˆ
0Bˆ
T and  
σˆ
0Aˆ
T  (3) 
and it was compared to the corresponding critical value, i.e. the corresponding quantile of 
Student's t-distribution. The value of 0.05 was adopted for the significance level of all the 
tests conducted within the experiment. 
Equality of the estimated plane parameters and their assumed values was not the only thing 
that was tested. The second test checked whether the corresponding parameters of the plane 
obtained from TLS data and the one obtained from the data acquired by the total station could 
be considered equal. The null and the alternative hypothesis of the corresponding plane 
parameter testing were: 
 
TLSTSa
TLSTS0
ii:H
ii:H
≠
=
, (4) 
where i stands for estimated A, B or C plane parameter. The value of the test statistic was 
obtained through: 
 Pandžić, J. et al.: The accuracy analysis of Leica ScanStation P20 data by means of… 105 
 
2
i
2
i
TLSTS
TLSTS
σˆσˆ
ii
T +
−=  (5) 
with 
TS
2
iσˆ   and TLS
2
iσˆ   being the corresponding variances of an observed plane parameter. This 
test statistic was compared to the corresponding critical value, i.e. the corresponding quantile 
of Student's t-distribution. 
 
3 RESULTS AND DISCUSSION 
 
As already mentioned, plane parameters and their standard deviations were estimated by 
using the ordinary least squares method. Total of four planes were fitted to the point data: left 
and right plane (Fig. 3) for both the TLS data and the data acquired by the total station. The 
plane parameters along with their standard deviations used in statistical testing are given in 
Table 1. 
 
Table 1 Plane parameters and their standard deviations 
Plane Data source Aˆ  Bˆ  Cˆ  Aσˆ  Bσˆ  Cσˆ  
left TS 0.0008 0.0021 3092.888 0.0015 0.0003 7.429 TLS 0.0003 0.0015 3095.623 0.0011 0.0002 5.540 
right TS -0.0042 -0.0003 3119.097 0.0025 0.0006 12.709 TLS -0.0040 -0.0012 3117.799 0.0023 0.0005 11.710 
 
In case of the left plane, statistical testing of the equality of the estimated plane parameters 
and their corresponding assumed values showed that, for the adopted significance level of 
0.05, it cannot be claimed that the estimated plane is parallel to the YZ plane of the coordinate 
system. As opposed to the parameter B, parameter A was found not to be significant, thus 
implying that the plane is rotated only about the Y-axis, i.e. that it is not vertical. This 
conclusion is valid for both planes, TLS and TS. Statistical testing of the equality of the 
corresponding plane parameters was also done. Plane fitted to the TLS data proved to be 
statistically equal to the plane fitted to the TS data for the chosen significance level of 0.05. 
Similarly, the right TLS plane proved to be slightly inclined, i.e. rotated only about the Y-
axis of the coordinate system and consequently nonvertical. On the other hand, in case of the 
right TS plane, both parameters, A and B, proved not to be significant for the adopted 
significance level of 0.05, thus implying that the plane is vertical, as well as parallel to the YZ 
plane of the coordinate system. As in case of the left plane(s), statistical testing did not show 
significant difference between the plane fitted to the TLS data and the plane fitted to the TS 
data. 
 
4 CONCLUSION 
 
Rapid development of terrestrial laser scanning urged the question whether this surveying 
technique is convenient for using in particular engineering tasks. Although majority of 
surveyors still tend to rely on traditional methods such as surveying by using a total station, 
TLS slowly establishes itself as an equally suitable surveying method, if not better in some 
cases. This paper was an attempt to shed light on the issue of accuracy of the data acquired by 
the terrestrial laser scanner Leica ScanStation P20, by comparing this data to that acquired by 
the laser total station. 
106 INGEO 2014 
 
 
 
The idea was to assess to which extent quality of data acquired by using two different 
techniques could be considered comparable. Two construction planes of the building of the 
Faculty of Civil Engineering in Belgrade were chosen as basis for the analysis. The planes 
were fitted to the data by the means of the ordinary least squares method. Further statistical 
testing involving the estimated plane parameters and their standard deviations showed no 
significant difference between planes fitted to the TLS data and those fitted to the TS data for 
the adopted significance level of 0.05. Yet, all construction planes except for the right TS 
plane proved to be slightly inclined (not ideally vertical) for the same significance level. 
Still, methodology applied within this paper has some drawbacks reflecting primarily in 
impossibility of claiming certain accuracy of TLS. All one could claim is to which extent the 
results of scanning agree with those of surveying using total station. Furthermore, restrictions 
in the form of instrument errors, structure being scanned itself (material of which it was built, 
its roughness), number of points used for plane fitting, as well as dependence of the X 
coordinate on the Y and Z coordinates should be considered. 
 
 
ACKNOWLEDGEMENT 
This paper is the result of the authors' activities on the project TR36009, supported by the 
Ministry of Education, Science and Technological Development of the Republic of Serbia. 
 
 
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The Accuracy Analysis of Leica ScanStation P20 Data by Means of Point Cloud Fitting Algorithm

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The Accuracy Analysis of Leica ScanStation P20 Data by Means of Point Cloud Fitting Algorithm

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