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Accuracy Analysis of Railway Mapping - the Stuttgart Strassenbahnen Pilot Project
Master thesis
By: Nahla Mohammad Abdelkader MAHMOUD
Co-supervisor: Dipl.-Ing. Michael Peter
Supervisor: Prof. Dr.-Ing. Dieter Fritsch
June 2014
Institute for Photogrammetry
University of Stuttgart
1. Introduction and motivation:
The railway companies plan to monitor their railway infrastructures information for
maintenance and examination purposes (Arastounia, 2012). Regarding to the development of
Mobile Mapping Systems (MMS) for country-wide railway mapping, afterwards a logical
step is to use these systems for precise monitoring of city railway systems (trams).
The mobile laser scanner system integration in addition to the capturing of point cloud data
Stuttgart railway were performed by consortium of an academic institute, mapping industry
and the Stuttgarter Straßenbahnen AG. They use the Stuttgart StrassenBahnen platform for the
simultaneous acquisition of laser measurements.
This research work aims to:
Measure precision of the mobile laser scanner system and accuracy of their collected
point clouds.
Extract of Interior infrastructure information of railway from the Geo-referenced Point
Clouds by two different software packages.
Cyclone of Leica Geosystems
CARD/1 of IB&T
Determine the deviations between the vector lines extracted from the point clouds and
the SSB existing ones.
2. Literature Review and system Integration
Basically, the idea of laser scan is concluded in measuring the distance between the laser
scanner and the object. The laser scanner shoots a laser beam to the object and measure the
elapsed time that takes the laser beam to return back. The distance is computed by dividing
the time by two and multiplying it by the speed of light. Then, the coordinates x, y and z can
be calculated using the measured distance, the bearing (horizontal angle from a known line) to
the object, and the vertical from gravity to the object (Solutions, 2014). Mobile laser
scanning applications are completely different from laser scanning done by the terrestrial
static method. In terrestrial static method, the 3D point clouds are collected from one distance
and two angle measurements (laser beam around the horizontal and vertical axes).
(Mettenleiter, et al., 2008)
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Figure1: MLS system integration mounted on tram, Stuttgart
(SSB-IGI, 2013)
As seen in Figure 1, the laser scanner is fixed on a movable platform worked as a part of a
kinematic multi-sensor system. Therefore, the position of laser scanner is varying during the
point cloud collecting. The measurements have a deflection in the vertical direction only. In
this case, the 3D measurements can be determined from the distance measuring, one angle
measurement and variable position of the laser scanner. (Mettenleiter, et al., 2008)
Figure 1 : Principle of mobile laser scanning, the scanner fixed on a moveable platform.
(Mettenleiter, et al., 2008)
After accomplishing a high efficiency of mobile laser scanning system for roadway and
cadastral mapping application, the system is recommended to be selected for extraction the
information of railway. Stuttgart Railway Company planes to monitor their internal
infrastructure. Therefore, Stuttgart Strassenbahnen pilot project is developed by consortium of
an academic institute, mapping industry and the Stuttgarter Strassenbahnen AG. The mobile
laser scanner system is integrated on SSB tram. Point cloud data of this study is collected at
July 2013 by a. The study covers length of about 4 km. The main components of used mobile
laser scanning system of SSB Project are:
Moveable platform (SSB
Tram)
2 Zoeller & Froehlich
Laser Profilers 9012
the GNSS/IMU system
Corrsys L 350 odometer
Video system
Figure 2 shows the used tram with the
different sensors, which were built on
metallic platform fixed in front of the
tram during the measurements.
3. Software:
In this study, three main software
packages [Cyclone, CARD\1] are used to finalize the process of mobile laser scanner data.
Cyclone software is developed by Leica Geosystems which provides a high-performance
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environment for manipulating point cloud data captured by High Definition Surveying (HDS)
systems. Cyclone provides the user to the accessibility to obtain an accurate visualization,
navigation, measurement, and model 3D objects and scenes (Leica Geosystems, 2005).
Cyclone is developed especially for point clouds data and has several tools and functions. So,
the following information indicates the used main functions and tools which have been used
in the study of mobile scanning data. Figure 3 concludes the used main functions during the
processing methodology.
Figure 3: Cyclone software package main functions
CARD\1 software was developed by IB\T (Ingenieurbüro Basedow & Tornow) team in
Hamburg as CAD drawing. It supports the work on surveying, planning routes or presenting
projects. Since this research deals only with laser scanner data, CARD\1 provides some tools
to obtain object heights, digital terrain models, ground profiles and cross-sections from point
clouds. CARD\1 visualize point clouds in some working views, such as in base map, the
profile, the cross-sectional and the 3D project view (IB&T, 2014).
By using CARD\1 software, there are sufficient tools enough to manage and extract
information form point cloud. The next sections indicate the functions of those tools and how
they used, see Figure 4.
Figure 4: CARD\1 software package main functions
4. Infrastructure Extraction of Railway:
Interior infrastructure that should be extracted are edges, cable tub edges, CityRail steering
power lines, rail surfaces and rail axes. The laser scanning data of Stuttgart Strassenbahnen
(SSB) Project is not dense enough to extract the horizontal longitudinal and cross-section
Cyclone used main functions
Importing &
exporting data Registration Modelling Animation
CARD\1 used main functions
Import & manage
point cloud data
Design and edit
cross-sections
Generate topography
from cross-sections
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lines. Cyclone is developed for laser scanner products so a lot of tools are available for
drawing, editing and adding properties for extracted lines to separate between them easily.
Figure 5 shows the 3D view of the used mobile scanning measurement data. The mobile
scanning data is stored in LAS format. Each point cloud is available with its position (X, Y,
and Z coordinates) and its intensity value of the reflected beam. No color information (RGB
value) is given from that data. The black gaps through the collected data might be come from
the shadow effect. Figure 6 and Figure 7 indicate to the complete infrastructure information
which has been extracted from mobile laser scanning data of Stuttgart project.
Figure 5: 3D view of mobile scanning data
Regarding CARDT\1, the laser scanner data is presented in base map as 2D view, while the
cross-section window provides the view of absolute height (elevation) [in meter] as Z-value
and offset from the Alignment [in meter] as T-value. Therefore, the interior information of
point clouds (e.g. railheads, edges and power lines) appear in more details in cross-section
window. Figure 8 show the 2D view of point clouds with all extraction lines.
Figure 6: Cyclone extracted infrastructure Figure 7: Top view of extracted line of Marienplatz’s tunnel
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Figure 8: 2D view of point cloud data with all extraction lines
5. Point cloud transformation:
The integration GNSS/IMU systems with 2 Zoeller & Froehlich Laser profilers in the mobile
scanning of Stuttgart pilot project produce geo- referenced point clouds in UTM coordinate
system. In some regions (e.g. tunnels and underground stations) the GNSS/IMU systems do
not provide accurate geo-referenced point clouds where no GPS signal available ( Williams,
et al., 2013). Furthermore Black/White targets have been surveyed using traditional methods
in Gauss-Kruger (GK) coordinates.
The transformation of 3D measurements from UTM to Gauss-Kruger coordinates increases
the accuracy level of MLS point cloud, thus the 7 parameters transformation (Helmert
transformation) has been applied. Helmert equation can be written as following:
[ ]
[
] ( ) ( ) ( ) [ ]
where:
X, Y, Z : target system coordinates GK in this case,
x, y, z : start system coordinates UTM in this case,
Tx , Ty, Tz :translation in x, y and z respectively,
λ : scale factor;
R1(α), R2(β), R3(γ) : rotation matrices.
The mean error of black and white targets after Helmert’s transformation into GK coordinates
is around 47 cm due to a failure of DGPS/IMU system during data collection as to be seen in
table 1.
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Table 1: Error of control points
Target ID Error in X [m] Error in Y [m] Error in Z [m] Mean [m]
7565165 -0.418 -0.113 0.054 0.436
7565166 -0.264 -0.065 0.039 0.275
7565168 0.682 0.178 -0.093 0.711
The targets simulated correction is created to correct the point cloud’s extractions as fellow:
1. Extraction of an approximate trajectory of the tram in Cyclone by select the 20
positions with interval distance 3m between them.
2. Exporting the coordinate measurements of the visible targets in point cloud
and in TXT file.
3. Assuming that the difference between the target from point cloud and the other which
surveyed by SSB is expressed in a linear equation. The translation and drift of linear
equation are depending on the trajectory’s independent parameter t as follow in
equation:
where:
, and : the translation in x, y, z respectively,
, and : the drift in x, y, z respectively,
: the independent parameter ,indicate the cumulative distance of the
laser scanner position from assumed starting point to the closest position of acquiring the
target.
Table 2 indicates that the mean error of targets is 4.5 cm after applying the simulated
correction.
Table 2: the targets accuracy after simulated correction
Target ID Error in X[m] Error in Y[m] Error in Z[m] Mean[m]
7565165 0.0596 0.0214 -0.0134 0.0647
7565166 -0.0643 -0.0232 0.0145 0.0698
7565168 0.0048 0.0017 -0.0011 0.0052
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Figure 9: Approximated trajectory and its correction
Figure 9 shows the approximated trajectory from point cloud data and the new one after
adding the correction. It is clear from this figure, that there is an improvement in the trajectory
by using this correction. Figure 10 illustrates the error values from Helmet’s transformation
and after applying simulated correction.
Figure 10: Target’s error after and before the simulated corrections
6. Noise level of collected point clouds:
The noise level of point cloud data measures overall quality of used laser scanning system.
This precision can be estimated by fitting best planes from scanning data. The plane surface
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should be homogenous where the different levels in wall or impurities in concrete inside the
tunnel cause very bad quality. By fitting more than 10 planes through homogenous surface
like station’s ground and wall, the precision of laser scanning system is 2 mm in
correspondences with the window’s measure as seen in Figure 11.
Figure 11: Fitting plane from homogeneous station’s wall
7. Accuracy analysis of extracted vector lines:
The infrastructure information of railway is extracted from different software (CARD\1 and
Cyclone). As well as these vector lines are existing by SSB Company in CAD file.
1. Cyclone & CARD\1 Comparison: Figure 12 shows the two different sources of
vector information extraction, which are almost identical. The distance between the
railway axes fulfils the standard distance (1.435) with accuracy 5 mm. The maximum
deviation between two extractions reaches to 1 cm in some sections
Figure 12: Comparison between CARD\1 and Cyclone extractions
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2. Extracted point cloud’s lines and SSB existing one comparison: CAD data of
tunnels is delivered by SSB in Gauss-Kruger coordinates. It is necessary to see the
deviations between the vector lines extracted from the point clouds and the existing
ones.
Figure 13 illustrates the comparison of SSB CAD data and the extracted vector lines
from point cloud data. The comparison length is divided into 8 sections to see how
evaluated error through the data length. And also the reasonable source controlled on
the error. Moreover, Table 3 shows the error value for selected sections.
Figure 13: SSB data via the extracted from point cloud
Table 3: Error in selected sections from SSB& Extracted comparison
Section ID Error [m] note
1 0.087
2 0.005 Beside Target 7565166
3 0.117
4 0.204
5 0.203
6 0.110
7 0.006 Beside Target 7565168
8 0.105
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8. Conclusion:
SSB Company is looking for scanning their railways for monitoring or checking the
defections through them. Thus, consortium of academia, mapping industry and the Stuttgarter
Straßenbahnen (SSB) started a pilot study on July 5, 2013 for applying the mobile laser
scanning system to collect the point cloud data. The study covers length of around 4 km. If the
preliminary results of this pilot study were satisfying, then they will generalize the mobile
system for all their railways.
In this research, the processing is completed for a length around 250 m for MarienPlatz area.
The main motivations of research are to measure the quality of used scanner and their point
clouds as well as the accuracy of extracted vector lines from point clouds which are collected
from kinematic system. Two software packages are used to reconstruct the infrastructure
information of railway from point cloud data, (Cyclone and CARD\1). Both of them deliver
accuracy around 5 mm in extraction. The precision of laser scanner is 2 mm which is obtained
from best planes fit in homogenous surfaces.
Despite there is a low accuracy visible, it is expected that the number of targets is sufficient
for reliable results if the DGPS/IMU system will work properly. If not then additional targets
will improve the accuracy of extraction of infrastructure information from mobile scanning
data.
9. References:
Arastounia, M., 2012. Automatic classification of LIDAR point clouds in a railway
enviroment,Master thesis. Netherlands: faculty of geo- information science and earth
observation of the university of Twente.
IB&T, I. B. &. T. G., 2014. CARD\1. [Online]
Available at: http://www.card-1.com/en/product/overview/
[Accessed 16 May 2014].
LeicaGeosystems, 2014. leica Geosystem. [Online]
Available at: http://hds.leica-geosystems.com/en/Leica-Cyclone_6515.htm
[Accessed 15 May 2014].
Mettenleiter, M. et al., 2008. 3D Laser Scanner as Part of Kinematic Measurement
Systems. Wangen, Germany, International Conference on Machine Control &
Guidance.
Solutions, S., 2014. SurvTech Solutions. [Online]
Available at: http:// floridalaserscanning.com\3d-laser-scanning/how-dose-laser-
scanning-work/
[Accessed 07 May 2014].
SSB-IGI, 2013. Railmapper Stuttgart Strassenbahnen Pilot Project provided data.
Stuttgart: IGI & Stuttgarter Straßenbahnen.
Williams, K., Olsen, M. J., Roe, G. V. & Glennie, C., 2013. Synthesis of
Transportation Applications of Mobile LIDAR. Remote Sensing, Volume 5(9), pp.
4652-4692.
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