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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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