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MYU Tokyo
S & M 3219

Space-constrained Mobile Laser-point Cloud Data
Acquisition MethodFengzhu Liu,1,2 Bogang Yang,1,2* Ying Yang,3** Yali Zhao,1 and Xi Zhai3
1Beijing Institute of Surveying and Mapping, Beijing Institute of Surveying and Mapping,No. 15, Yangfangdian Road, Haidian District, Beijing 100038, China
2Beijing Key Laboratory of Urban Spatial Information Engineering,No. 15, Yangfangdian Road, Haidian District, Beijing 100038, China
3National Geomatics Centre of China, 28 Lianhuachi West Road, Haidian District, Beijing 100830, China(Received October 31, 2022; accepted February 20, 2023)

Keywords: mobile laser-point cloud, ground laser-point cloud, data fusion, indoor mapping, outdoor
mapping

The mobile laser-point cloud measurement system has been widely used in urban highprecision mapping. In some urban spaces with many high-rise buildings and indoor spaces,
high-precision point cloud data cannot be directly obtained due to weak or no global navigation
satellite system (GNSS) signals. We propose a space-constrained mobile vehicle-mounted laser
scanning data acquisition plan that can effectively utilize the advantages of ground 3D laser
scanning and mobile vehicle-mounted laser scanning. In addition, the plan adopts multi-source
point cloud data fusion technology to obtain high-precision complete point cloud data bothindoors and outdoors. The validity and reliability of the plan are verified by data acquisition
experiments, which provide a useful reference for future high-precision mapping.

1.

Introduction

The rapid development of mobile measurement systems has the characteristics of fast dataacquisition, low cost, and easy management and storage. Mobile measurement systems haveachieved great success in quickly obtaining indoor and outdoor space information throughcombined sensors. Two main types of mobile 3D laser scanning systems are used: one is asimultaneous localization and mapping (SLAM)-based mobile 3D laser scanning system, andthe other is a vehicle-mounted 3D laser scanning system based on high-precision inertial
navigation.(1–3) The two types have different characteristics and advantages. The SLAM-based
mobile 3D laser scanning system is suitable for indoor and outdoor areas with a small spatial
range.(4–6) The vehicle-mounted 3D laser scanning system based on high-precision inertial
navigation is a device that integrates multiple sensors to collect information and quickly obtain3D surface information.(7–9) This system can be applied to the acquisition of indoor and outdoor
3D spatial information. The inertial navigation system (INS) is an autonomous navigation
system that does not depend on the external environment. It has the advantages of high*Corresponding author: e-mail: bogangy@126.com**Corresponding author: e-mail: yingy@whu.edu.cn

https://doi.org/10.18494/SAM4223

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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positioning precision in a short time and powerful anti-interference capability. It primarily relies
on high-precision inertial navigation and odometer assistance for combined positioning, which
can significantly improve GPS. The precision of the integrated navigation system in the unlocked
state can be applied to indoor mapping.(10–13)
Combining the results of existing research and technological development, we proposed adata acquisition method using the combination of ground and vehicle-mounted laser scanningtechnology and applied it to the mapping of underground garages by summarizing and analyzing
the existing methods. The vehicle-mounted laser scanning system based on high-precisioninertial navigation quickly obtained a large range of indoor and outdoor data and directly
obtained the point cloud in the geodetic coordinate system. In the area without a GNSS signal,
the accuracy of the point cloud decreases with the time when the GNSS is out of lock. However,terrestrial 3D laser scanning has the advantages of convenient station setup and high relative
precision. Therefore, we propose a data acquisition plan that integrates the mobile vehiclemounted laser scanning system and the ground 3D laser scanning and combines the two methods
to give full play to their respective advantages to quickly obtain a point cloud. In addition, we
introduce the specific design process, data acquisition, and processing methods of the plan, andfinally verify the precision of the data obtained, which meets the application requirements ofindoor mapping.

2.

Data Acquisition Plan

The plan was tested in a parking lot in a building of a business district. The vehicle-mounted
laser scanning device was used to obtain data, and then the ground laser data were obtained
according to the scanning results. The specific process is diagrammed in Fig. 1.
The field survey clarifies the ground traffic conditions, lighting conditions, and parking lot
entrances and exits to ensure the smooth progress of data acquisition. Route planning is mainly
based on the actual situation of the underground space and instrument parameters, and areasonable route is designed to fully cover the survey area, avoiding long-term parking during
the scanning process, and reducing the repeated scanning of sections.

Fig. 1.

Data acquisition plan.

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2.1

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Acquisition of vehicle-mounted laser scanning data

The vehicle-mounted laser system integrates multiple sensors to collect information andquickly obtain 3D information on the surface.(14,15) Through the combined calculation of GNSS,
INS, and odometer data, the trajectory and attitude of the carrier are determined, and the
distance and angle information obtained by the laser scanner are combined to obtain the 3D
points of the measured object. When data acquisition is carried out in the outdoor part of acommercial area, the GNSS signal is weak due to the height and density of commercial
buildings, and there is no GNSS signal during the data acquisition process in an undergroundparking lot. In contrast, the INS is an autonomous navigation system that does not depend on theexternal environment. The system has the advantages of high positioning precision in a shorttime and a strong anti-interference capability. Therefore, it mainly relies on high-precisioninertial navigation and odometer assistance for combined positioning, which can significantly
improve the precision of the integrated navigation system under conditions when the GPS loses
lock. The laser scanner used in the vehicle-mounted laser system in this work was Riegl’s VUX1HA. Its main parameters are a range from 2 to 200 m, 360° omnidirectional scanning, 250 rps,
and a scanning point frequency of up to 1017 KHz. To reach the millimeter-level of precision, weused an INS, which is a domestic high-precision system. Its main parameters are a drift of
0.01°/h, a combined attitude measurement precision heading better than 0.008°, a pitch better
than 0.005°, and a roll better than 0.005°.
Usually, a vehicle-mounted laser system is installed above the vehicle. Owing to the height
limit in the underground parking garage, the vehicle-mounted laser system should be integrated
into a tricycle platform to pass the garage entrance. The system integration is shown in Fig. 2.
Before the operation, the driving route must be designed, and a straight line route should beplanned as much as possible to avoid vehicles parked in the garage. Data are acquired according
to the design route. During the operation, it was necessary to use GPS and INS for initialalignment outdoors to obtain accurate initial position coordinates and attitude. During operation

Fig. 2.

(Color online) Vehicle-mounted laser system.

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in the basement, high-precision INS and odometer data are used to calculate the position andthen to obtain the attitude and position information of the device at each moment. The 3D
coordinates of the object being measured are calculated. After the data scanning is completed,the alignment is completed outdoors again, and the round-trip two-way solution and adjustment
processing are realized.
2.2

Acquisition of ground laser scanning data

The ground laser scanner (Fig. 3) is in a fixed position, rotates 360° in the horizontal
direction, and obtains the point cloud in the laser reachable region. Using the distance measured
by the laser and the corresponding rotation angle, the 3D coordinates of the surface of the object
are directly obtained after calculation to form a laser point cloud. The device used for dataacquisition was Riegl’s VZ-400, which scans 360° × 260° at a scan rate of 1000000 points/s anda measurement precision of 5 mm.
The ground laser scanner only needs to place a few stations to complement the vehicle laser
data, but the point cloud obtained by the ground laser needs to use its high relative precision tocorrect the vehicle point cloud, so it is placed on the vehicle in the survey area during themeasurement process. The data from the laser scanning system are easily occluded, and thescanning yields a wide range of data.

3.

Data Processing

The data obtained by the ground laser scanner uses its own processing software for point
cloud generation and splicing, and different stations are scanned and spliced together to generate
a spliced point cloud.

Fig. 3.

(Color online) Ground laser scanner.

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GPS data, inertial navigation data, odometer data, and laser data collected by the vehiclemounted scanner need to be processed to generate high-density point clouds. The processincludes point cloud preprocessing, integrated navigation data calculation, and fusion of
integrated navigation results and laser data to generate point cloud data with 3D position andintensity information. The point cloud is then processed.
3.1

Point cloud data preprocessing

When the vehicle-mounted laser system acquires data, point clouds with uniform density can
only be obtained by moving at a uniform speed. Therefore, point clouds in the driving andvertical directions with the point cloud density will appear uneven when the vehicle stops or the
speed is uneven. The point cloud obtained by ground laser scanning is fan-shaped, and the closer
the scanner is to the object being measured, the greater the point cloud density. Owing to multipath reflection, dust, and other reasons, the acquired point cloud data inevitably contain noise. It
is necessary to preprocess the point clouds obtained by vehicle-mounted and ground laser
scanning to enable point cloud thinning and the removal of some noise points as well as points
that are far away and do not need to be matched.
Point cloud thinning is based on the method of downsampling at an equal distance of 3 cm
between points. The underground garage space was limited, so the scanner center distancethreshold was set to 60 m. The removal of noise points was processed by statistical analysis andfiltering of the neighborhood point cloud in the point cloud library (PCL). The point cloud was
also processed by setting a threshold of the mean and standard deviation to remove point cloudnose in the results as a threshold. The points whose average neighborhood distance is outside thestandard range can be defined as outliers and removed from the data. The main parameters wereanalyzed for each point, and the number of adjacent points and the standard deviation of multiple
thresholds were set to 50 and 1, respectively. The effects before and after processing are shownin Fig. 4. The vehicle point cloud and ground laser-point cloud after point cloud generation,
thinning, and denoising are shown in Fig. 5.

(a)

(b)

Fig. 4. (Color online) Comparison before and after denoising and thinning. (a) Before denoising and thinning. (b)After denoising and thinning.

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Sensors and Materials, Vol. 35, No. 3 (2023)(a)Fig. 5.

3.2(b)(Color online) Data map of point cloud. (a) Ground laser-point cloud. (b) Vehicle laser-point cloud.

Data fusion

According to the characteristics of the vehicle-mounted laser system, the precision of thepoint cloud will worsen with time, and the precision of the point cloud will also worsen as the
distance from the laser center increases. In the process of data fusion, we take advantage of therelatively high precision of ground laser scanning data for processing. A diagram of the flow of
the process is shown in Fig. 6, and the details of the process for data fusion for two different
point clouds are described in the following subsections.
3.2.1 Data registration
First, the pretreated vehicle-mounted point cloud is segmented according to the driving
distance. More than four corner points are selected as characteristic points (Fig. 7) in the point
cloud, and the ground laser point cloud is transformed from relative coordinates to WGS84
coordinates. Second, the ground laser point cloud in the WGS84 coordinate system is used as thereference point cloud to correct the vehicle-mounted trajectory. The point cloud is recalculated
for the modified trajectory, and a point cloud meeting the precision requirements is generated
after many iterative calculations. Finally, all the data in the survey area are processed.
The principle of trajectory correction uses the high-precision inertial navigation device in thevehicle laser system with characteristics of high stability over a short period of time. In the local
range, the control points and the points in the corresponding point cloud are linearly fitted, andthen the trajectory points are linearly fitted according to time. Corrections are made to generate
the correct trajectory. The point cloud data after coarse alignment are shown in Fig. 8.

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Fig. 6.

(a)935

Flow chart for data fusion.

(b)

Fig. 7. (Color online) Diagram of point cloud data conversion. (a) Ground-based laser-point cloud feature points.
(b) Vehicle-mounted laser-point cloud feature points.

Fig. 8.

(Color online) Point cloud after coarse alignment.

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3.2.2 Registration algorithm of iterative closest point (ICP)The ICP is the most popular registration algorithm for point cloud map registration and is an
optimal registration based on least squares. Through repeated iterations, a set of rotation
matrices R and translation vectors are found to prevent the convergence of matching errors, andthe coordinates of the data point set P and the model point set X are set. The equations are

P ={ Pi ∈ R, i =1, 2, … , N P }(1)

X = { X i ∈ R, i = 1, 2, … , N x }.

(2)

and

The mean square matching error between P and X isf ( x, p ) =1 NP
2
X i − Pi .
∑N i =1(3)

The point set pn+1 after n + 1 iterations can be expressed as+1
p n=
p n + qT .

(4)Another expression of the mean square error can be obtained using Eq. (3) asf ( R, qT ) =1 NP
2
X i − PR
− Pi .
∑i
N p i =1

(5)

If there is a set of rotation matrices R and translation vectors qT that make Eq. (5) converge,then the rotation matrices R and translation vectors qT are obtained. The ICP algorithm is used
after the rough registration of the vehicle point cloud and the ground laser-point cloud. The
initial position of the point cloud does not deviate significantly. Therefore, point cloud matching
can be accurately realized for the point cloud with a large amount of data. The registration isrealized by VS2010, C++ language configuration PCL under the WIN7 operating system
environment. Registered point clouds of a quadrate pillar in Fig. 9 illustrate the effect ofregistration of two different point clouds. The green vehicle point cloud and the blue groundpoint cloud are basically at the same position at the corner of the pillar.
3.3

Vector map

The point cloud data can be used as basic data for a vector map of the underground garage.
From the point cloud, the features of the underground garage can be clearly seen, such as thecorner of the building, the outline of the pillar, the position of the ground parking space, and theSensors and Materials, Vol. 35, No. 3 (2023)

Fig. 9.

937(Color online) Point cloud after ICP matching.

Fig. 10. (Color online) Vector map of underground garage.

arrow mark, as well as the pipeline above the underground garage. All the point cloud data have
3D coordinate information. The vector map (Fig. 10) obtained by the point cloud can provide abase map for later 3D modeling and can also provide more abundant geometric dimension
information, such as height.

4.

Precision Analysis

The precision is verified to obtain the root control points using a closed traverse survey andthen to set up a full-station electronic tachymeter on the root control points to measure thecoordinates of the characteristic points in the garage. Characteristic points include the corner
points of the upright posts, the corner points of the indicating arrows, and the corner points of
the room. The accuracy of the control point of the root of the graph meets the requirements of afourth-class traverse survey.
Mapping is carried out in the point cloud, and the precision of the point cloud directly
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Table 1Comparison table of point cloud and total station coordinates.
Point cloud coordinate
Total station coordinate
ID
x
y
z
x
y
z
D11
9346.381 8045.571
37.5669346.348 8045.547
37.567D129346.404 8048.352
37.6089346.393 8048.341
37.611D189336.001 8055.927
37.6939336.004 8055.931
37.675D199327.862 8055.836
37.7119327.960 8055.823
37.674D209319.901 8050.611
37.6219319.886 8050.626
37.596
D219327.953 8064.629
37.6969328.041 8064.660
37.690D229335.979 8064.595
37.6229335.978 8064.600
37.627

dx
0.033
0.011
−0.003−0.0980.015−0.0880.001

Difference
dy
0.024
0.011
−0.0040.013−0.015−0.031−0.005dz−0.001−0.003
0.018
0.037
0.025
0.006
−0.005

Fig. 11. (Color online) Diagram of error distribution.

before mapping to ensure the accuracy of the vector map. By loading the fused point cloud into
the point cloud display software, the point cloud coordinates corresponding to the position of thefeature points are collected, and the precision of the final point cloud data is obtained throughstatistical analysis. Table 1 is a list of some of the data used to evaluate precision and Fig. 11
shows the diagram of error distribution.
The comparison of the precision between the coordinates obtained by the feature point
traverse and the coordinates in the point cloud shows that the point cloud obtained by thismethod meets the needs of underground garage mapping.

5.

Conclusions

We analyzed the process of acquiring and fusing a vehicle-mounted laser scanning systemand the ground laser-point cloud data, explained the purpose of the plan, and verified thefeasibility of its use by statistics on the precision of the measurements. This plan had relatively
high requirements for the precision of the INS system of the vehicle-mounted laser system, but
the combined use of the vehicle-mounted laser system and the ground laser greatly improved theefficiency of indoor and outdoor mapping in a basement. The vehicle-mounted device directly
obtained the point cloud of the geodetic coordinate system to realize the integrated acquisition ofindoor and outdoor data. The proposed plan still requires additional work to be improved. First,

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other vehicle-mounted laser systems should be tested, such as a SLAM laser system, which is
widely used in indoor mapping. Second, the plan should be applied to more places (e.g., shopping
malls, streets, and parks) to verify its validity and applicability. Third, the research in this study
focuses primarily on data collection and real-time computing, and research on the visualization
strategy received less attention. Investigations on adaptive visualization strategies combinedwith multithreading architecture and the realization of rapid visualization under highconcurrency conditions will be targets of future research work.

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About the Authors		Fengzhu Liu received her Ph.D. degree from Wuhan University, China, in
2016. She has been working at the Beijing Institute of Survey and Mapping
and as a researcher at the Beijing Key Laboratory of Urban Spatial Information
Engineering, and since 2018, she has been a senior engineer. Her researchinterests are in urban 3D spatial data acquisition and processing.
(540508818@qq.com)

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Bogang Yang received his Ph.D. degree from Beijing Forestry University,
China, in 2006 and he was engaged in postdoctoral work at Peking University
from 2007 to 2009. Since 1983, he has been working at the Beijing Institute ofSurvey and Mapping, and since 2004, he has been a professor-level senior
engineer. He is the Director of Beijing Key Laboratory of Urban Spatial
Information Engineering. His research interests are in surveying and mapping
technologies, smart city construction, and urban spatial analysis.
(bogangy@126.com)
Ying Yang received his B.S. degree from Wuhan University, China, in 2007,
his M.S. degree from the Chinese Academy of Sciences, China, in 2010, and
his Ph.D. degree from Wuhan University, China, in 2017. From 2010 to 2013,
he was an assistant professor at the Chinese Academy of Surveying and
Mapping, China. Since 2017, he has been an engineer at the National Basic
Geomatics Center of China. His research interests are in photogrammetry,
UAV systems, and sensors. (yingy@whu.edu.cn)

		Yali Zhao received her M.S. degree from Capital Normal University, China,
in 2021. Since 2021, she has been working at the Beijing Institute of Survey
and Mapping, where she is an assistant engineer. Her research interests are in
geographic information science, land subsidence monitoring, and analysis
based on interferometric synthetic aperture radar (InSAR).
(18335156979@163.com)

		Xi Zhai received his B.S. degree in engineering of surveying and mappingfrom Shandong Agricultural University, China, in 2006 and his M.S. and
Ph.D. degrees in cartography and geographical information engineering from
Wuhan University, China, in 2012 and 2017, respectively. He is a senior
engineer at the National Geomatics Center of China. His research interests arein emergency mapping, sensor webs, and spatial knowledge services.
(zhaixi@ngcc.cn)