Journal of Digital Life 2023,3,8

ArticleDevelopment of Laser Scanner Units for UAV
Shigenori Tanaka 1, Ryuichi Imai 2, Kenji Nakamura 3, Yuhei Yamamoto 4, Yoshinori Tsukada 5
and Masaya Nakahara 6*
1

Faculty of Informatics, Kansai University, 2-1-1 Ryozenji-cho, Takatsuki-shi, Osaka 569-1095, Japan
Faculty of Engineering and Design, Hosei University, 2-33 Ichigayatamachi, Shinjuku-ku, Tokyo 162-0843, Japan
3
Faculty of Information Technology and Social Sciences, Osaka University of Economics, 2-2-8 Osumi, Higashiyodogawa-ku,
Osaka-shi, Osaka 533-8533, Japan
4
Faculty of Environmental and Urban Engineering, Kansai University, 3 -3-35 Yamate-cho, Suita-shi, 564-8680, Japan
5
Faculty of Business Administration, Setsunan University, Neyagawa-shi, Osaka 572-8508, Japan
6
Faculty of Information Science and Arts, Osaka Electro-Communication University, Shijonawate-shi, Osaka 575-0063, Japan
2

Abstract
In its i-Construction policy, the Ministry of Land, Infrastructure, Transport and Tourism has stipulated amanual for public surveying using by UAV photogrammetry with the aim of improving productivity atconstruction sites. However, UAV photogrammetry requires a huge amount of time to generate point clouddata from photographs, causing a problem that it is difficult to monitor the daily progress of the construction
site. There is another problem that it is incapable of taking measurements except daylight hours.
Against this backdrop, we have been developing laser scanner units for UAV equipped with LiDAR, IMU
and GNSS receiver. Then, we clarified the error factors that are expected to affect the precision prescribedof point cloud data and summarized the requirements for the onboard equipment and the method of generating
point cloud data. However, we have not yet proposed nor developed the laser scanner units as well as forgenerating point cloud data considering these requirements. Thus, in this paper, we developed the laser
scanner units and a new method for generating point cloud data within the laser scanner units to optimize theerror factors based on these requirements clarified in the existing study.
Keywords: i-Construction, construction site, unmanned aerial vehicle, laser scanner, point cloud data

1. Introduction
UAVs (Unmanned Aerial Vehicles) are used for various usages such as monitoring the progress of construction sites
or grasping the situation during disasters. In its i-Construction policy, the Ministry of Land, Infrastructure, Transport
and Tourism has stipulated a manual for public surveying using UAVs (draft) (Ministry of Land, Infrastructure,
Transport and Tourism. 2017b) with the aim of improving productivity at construction sites. This manual stipulates
how to generate 3D point cloud data (hereinafter referred to as "point cloud data") by UAV photogrammetry. However,
UAV photogrammetry requires a huge amount of time to generate point cloud data from photographs, causing aproblem that it is difficult to monitor the daily progress of the construction site . There is another problem that it isincapable of taking measurements except daylight hours.
To address these problems, use of a UAV-mounted laser scanner equipped with LiDAR (Light Detection And
Ranging), which is less sensitive to light intensity and capable of measuring point cloud data in a short time, has been
examined. In 2017, the precision prescribed for monitoring daily progress as well as construction management suchas the work progress using this equipment (Ministry of Land, Infrastructure, Transport and Tourism. 2017a) has been
determined. However, in order to satisfy the precision prescribed, it is necessary to clarify the characteristics of the
on-board equipment that affect the precision, the measurement conditions that consider the site co nditions, and themethod of generating optimal point cloud data.
Received: 2 April 2023, Revised: 27 April 2023, Accepted: 5 June 2023, Published: 3 August 2023
* Correspondence: nakahara@oecu.jp;
Publisher’s Note: JOURNAL OF DIGITAL LIFE. stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Copyright: © SANKEI DIGITAL INC. Submitted for possible open access publication under the terms and conditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1

Development of Laser Scanner Units for UAV
Tanaka, S., Imai, R., Nakamura, K., Yamamoto, Y., Tsukada, Y. and Nakahara, M.

Against this backdrop, existing studies have proposed a method using SLAM (Simultaneous Localization and
Mapping) (Sofonia, J., et al. 2019) and another method using IMU (Inertial Measurement Unit) and GNSS (Global
Navigation Satellite System) (Elamin, A., et al. 2022). The method using SLAM uses feature points in point clouddata to estimate and correct deviations in relative positions and tilts between point cloud data measured at different
times. However, the road and river embankment sites have a uniform shape in the longitudinal direction of theembankment, and there are few features. As there are few features in road and river construction sites, the relative
position and tilt deviation between point cloud data is misestimated when the method using SLAM is applied. In
contrast, when the method using GNSS and IMU is applied, the position and attitude of the aircraft can be measured
and corrected regardless of the presence or absence of features. Therefore, at a construction site, the method using
IMU and GNSS can generate the point cloud data of the entire measurement object with high accuracy.
We have been developing laser scanner units for UAV equipped with LiDAR, IMU and GNSS receiver (Tanaka, S.,et al. 2019). Then, we clarified the error factors that are expected to affect the precision prescribed of point cloud dataand summarized the requirements for the onboard equipment and the method of generating point cloud data. However,
we have not yet proposed nor developed the laser scanner units as well as for generating point cloud data considering
these requirements. Thus, in this paper, we developed the laser scanner units and a new method for generating point
cloud data within the laser scanner units to optimize the error factors based on these requirements clarifie d in theexisting study. Then, we evaluate the usefulness of the developed laser units by comparing it with the precision
required at the construction sites.

2. Problems and Development of Existing Equipment
2.1. Problems of the existing laser scanner units
Table 1 lists the components of the laser scanner units in the existing study (Tanaka, S., et al. 2019). In the existing
study, AMU-3002B Lite was used as the IMU. In dynamic environments, such as during UAV flights, the dynamic
accuracy of this IMU is less than 2 degrees. However, the internal time of the connected PC is given for themeasurement time of this equipment, while the time of the atomic clock of the GNSS satellite (hereinafter referred toas GNSS time) is given to the other LiDAR and GNSS receivers. A problem has come to light that small errors occur
between these times, causing the precision of point cloud data generation to decline. In addition, it has also been foundthat the measuring precision in the direction of elevation is not sufficient based on the precision prescribed in the
Guidelines for Work Progress Management (Dekigata Kanri Yoryo) (Ministry of Land, Infrastructure, Transport andTourism. 2017a).
Table 1. Specifications of equipment in past and present research
Equipment

UAV

LiDAR

IMU

GNSS

Past ResearchPresent Research (Developed Laser Scanner Unit)
Spreading Wings 1000

Matrice 600(Da-Jiang Innovations Science and Technology Co., Ltd.)(Da-Jiang Innovations Science and Technology Co., Ltd.)GNSS Receiver × 1

GNSS Receiver × 3

VLP-16 Lite (Velodyne Lidar,Inc.)

VLP-16 Lite (Velodyne Lidar,Inc.)

Accuracy in dynamic environment: 0.03 m

Accuracy in dynamic environment: 0.03 m(Standard deviation)(Standard deviation)

AMU-3002B Lite (Silicon Sensing Systems Japan Ltd.)

CSM-MG200 (Tokyo Aircraft Instrument Co., Ltd.)

Accuracy in dynamic environment: 2 degrees

Accuracy in dynamic environment: 0.5 degrees(Standard deviation)(Standard deviation)

u-blox M8P with SOI (i System Research Corp.)

Septentrio AsteRx-m2a with SOI (i System Research Corp.)

Accuracy in dynamic environment: 0.02 m

Accuracy in dynamic environment: elevation 0.01 m,(Standard deviation)

horizontal 0.006m(Standard deviation)

Copyright: © SANKEI DIGITAL INC.

2

Development of Laser Scanner Units for UAV
Tanaka, S., Imai, R., Nakamura, K., Yamamoto, Y., Tsukada, Y. and Nakahara, M.
Antenna for GNSS

60m

IMU

LiDAR

GNSS Module

Laser Scanner Units

Fig.1. Developed laser scanner units for UAV

Fig.2. Flight route2.2. Development of the laser scanner unitsFirst, to address the first concern, we adopted the CSM-MG200 made by Tokyo Aircraft Instruments Co., Ltd. as an
IMU that is capable of adding GNSS time as the measurement time. In dynamic environments, the dynamic accuracyof this IMU is less than 0.5 degrees. To address the second issue, we adopted the Septentrio AsteRx-m2a with SOI
made by i-Systems Research Inc., having a positioning accuracy with a standard deviation of 0.01 m in the verticaldirection and 0.006 m in the horizontal direction, as the GNSS receiver. As this GNSS can measure with a dynamic
accuracy of 0.01 m or more compared to the GNSS used in existing studies, higher accuracy can be expected indynamic environments, such as during a UAV flight. In the previous study (Tanaka, S., et al. 2019), furthermore, thepoint cloud data was generated by using only the data measured at stable flight attitude to achieve high precision. For
this purpose, we adopted Matrice 600, which is the successor to the UAV adopted in the previous study. This UAV isequipped with more GNSS receivers than the Spreading Wings 1000, enabling more stable flight. In this study, wedeveloped the laser scanner units as shown in Fig. 1 using these newly adopted devices and Velodyne's VLP-16 Lite.
As in the previous study, the tilt of the body (Roll angle and Pitch angle) is measured by the IMU, and the nose
direction (Yaw angle) is measured by two GNSS antennas. This arrangement is expected to be able to measure withhigher accuracy than existing approaches while the UAV is in flight.
3. Problems of the Existing Method and Solutions
3.1. Problems of the existing method of generating point cloud data within laser scanner units
In the evaluation experiment of the existing study, we clarified three problems in the existing method o f generating
point cloud data (Nagai, M., et al. 2009, Wallace, L., 2012, Martin, C., 2017). The first problem is that the measuring
precision declines when the flight speed is not stable. In the measurement by the laser scanner units, it flies as shownby the green line in Fig. 2. In this case, the UAV pauses at each turnaround point. It accelerates and decelerates
immediately before and after the pause, and the measurement data measured during the acceleration and deceleration
contain many errors.
The second problem is that the measured position of the GNSS receiver is used as it is, which causes deviation in thevertical direction. The measured values of the GNSS receiver deviate as much as the amount of the measuring
precision. Therefore, measurement errors occur in all the points of the point cloud data measured by LiDAR
corresponding to the measuring precision. However, it is expected that not only the GNSS receiver but also IMU
similarly cause a deviation for the amount of the measuring precision, all owing the similar problem to occur.
The third problem is that the generating precision of point cloud data declines due to the inclusion of points where thedistance to the object is far and the incident angle of the laser beam is low. In measurements by the laser scanner units,
data measured along near and far straight flight routes from the measurement point are mixed, and the generating
precision of point cloud data is reduced, as shown by the green line in Fig . 2.

Copyright: © SANKEI DIGITAL INC.

3

Development of Laser Scanner Units for UAV
Tanaka, S., Imai, R., Nakamura, K., Yamamoto, Y., Tsukada, Y. and Nakahara, M.
3.2. Solutions for the problems
The first problem is addressed by not using the measurement data during acceleration and deceleration for generating
point cloud data. The second problem is addressed by using a reference point for adjustment to make correction. In
aerial laser surveying and UAV laser surveying, the elevation is obtained using GNSS observation results. Thus, anadjustment reference point (hereafter referred to as "adjustment point") is set at a flat location where there is nothingaround that obstructs the sky. Subsequently, the deviation of the point cloud data in the direction of elevation isadjusted (Ministry of Land, Infrastructure, Transport and Tourism. 2017b) according to the adjustment point andresolved. At this time, multiple adjustment points are set to prepare the pla ne for adjustment (hereinafter referred toas the "adjustment plane"), in order to correct the deviation caused by IMU similarly as well as the GNSS receiver.
The problem can be solved by correcting the tilt of the measurement data according to this adjust ment plane. The thirdproblem is addressed by removing the point cloud data with low generation precision.
4. Proposal for a Method of Generating Point Cloud Data within Laser Scanner Units
4.1. Overview of the method of generating point cloud data within laser scanner units
In this paper, we propose a method of generating point cloud data to solve the problems of the existing method, using
the measurement data of the developed laser scanner units. Fig. 3 shows the flow of the developed method withinlaser scanner units. The method is composed of the functions of flight condition determination, time synchronization,
point cloud data generation, calibration, and the route profiling. The method allows generation of point cloud data of
the flight range based on the location information of the adjustment points, from the data measured by LiDAR, IMU,
and the GNSS receiver mounted on the laser scanner units.
4.2. Function of flight condition determination
This function is used to determine and extract the data measured during a uniform linear flight. Specifically, themovement trajectory of only the Fix solution is obtained from the GNSS receiver. The flight speed is calculated fromthe movement trajectory, and only the measurement data of each device during the uniform linear flight is extracted
for further processing. This function solves the first problem of the existing method.
4.3. Function of time synchronization
This function is used to synchronize the times of measurement data obtained by LiDAR, IMU and the GNSS receiverduring a uniform linear flight extracted by the function of flight condition determination. In specific, based on the
GNSS time of each device, the IMU measurement data measured at the closest time in each LiDAR measurement
point is synchronized as data measured at the same time. The location information obtained by the GNSS receiver iscalculated by linear interpolation from the pre- and post-data measured at the closest time to the current time andsynchronized.
4.4. Function of point cloud data generation
This function is used to generate point cloud data using the measurement data of LiDAR, IMU, and the GNSS receiversynchronized by the time synchronization process. In specific, it performs rigid-body conversion to the coordinate
system based on latitude, longitude, and elevation for each 3D measurement point by LiDAR using the Roll and Pitch
angles by IMU and latitude, longitude, elevation and Yaw angle by GNSS receiver. Then, point cloud data is generated
by superimposing all the points after the rigid-body conversion.
4.5. Function of calibration
This function is used to correct the measurement errors caused by IMU and the GNSS receiver, using the pre-measured
adjustment plane. First, the point cloud data after rigid body conversion is divided in units of LiDAR measurement
frame. In the case of LiDAR used in this paper, the time required for one round of measurement is 0.1 second; therefore,
the point cloud data is divided in units of 0.1 second. Next, the point cloud data for the frame measuring the adjustment
plane is obtained. Then, an approximate plane is generated in the point cloud data, which is corrected according to theangle of the adjustment plane. At this time, other frames are also corrected at the same angle. Finally, the height ofthe point cloud data is corrected according to the elevation values of the constituent points of the adjustment plane.
The point cloud data of the other frames are repeatedly corrected so that the elevation values within the range ofoverlapping with the point cloud data after correction can agree with each other. This function solves the second
problem of the existing method.

Copyright: © SANKEI DIGITAL INC.

4

Development of Laser Scanner Units for UAV
Tanaka, S., Imai, R., Nakamura, K., Yamamoto, Y., Tsukada, Y. and Nakahara, M.
• Measurement Data by LiDAR• Measurement Data by IMU• Measurement Data by GNSS
• Coordinate of adjustment points
Grid division ofentire point cloud data

Function offlight condition determination

Function of calibration

Function oftime synchronization

Function of route profiling

Function ofpoint cloud data generation

Route A
Route B• point cloud data of
the flight range

Fig.3. Developed method within laser scanner units

Route B is closer thanRoute A.
Measured on Route A
Point cloud data is removed.

Fig.4. Function of route profiling4.6. Function of route profilingThis function is used to do filtering by removing the data other than the point cloud data measured from the flightroute that is close from the measurement point. Fig. 4 shows the image of this process. First of all, the entire point
cloud data is divided into grids of a certain size. Next, the nearest route flown in uniform linear motion is searched
for each grid. Then, by eliminating the point cloud data measured from those other than the searched route, only the
point cloud data with close measurement distance and high incident angle is extracted. This function solves the thirdproblem of the existing method.
5. Verification Experiments
5.1. Experiment overview
In this experiment, we confirmed the usefulness of a developed laser scanner unit by using it to measure the Nomac
Drone Field in Nose-cho, Toyono-gun, Osaka, as shown in Fig. 2. The flight route was set to the route shown by thegreen line in Fig. 2, and the flight speed was set to 4 m/s. The adjustment plane was measured by network -type RTK
positioning. The network-type RTK positioning was performed using a PENTAX G3100 R2. The usefulness of thelaser scanner unit was evaluated by comparison with the point cloud data with the most probable values. The mostprobable values were measured using a ground-based laser scanner, PENTAX S-3180.
5.2. Evaluation method
In this experiment, the usefulness of developed laser scanner unit is evaluated by the difference in elevation between
the most probable value and the point cloud data by the laser scanner unit. Fig. 5 shows an image of the evaluation
method. First of all, both point cloud data right under the route by each route during uniform flight are extracted forthe period of 10 minutes (Upper part of Fig. 5). Next, among the obtained point cloud data, the point cloud data forwhich the vertical distance from the flight route to the horizontal direction is within 0.05m is extracted (lower leftpart of Fig. 5). Then, the two nearest points with the most probable value from the extracted laser scanner unit points
are selected. Finally, the vertical distance from the line segment connecting the two selected points to the laser scannerunit point is calculated, and the difference in elevation between the most probable value and the point cloud dataobtained by the laser scanner unit is evaluated from their maximum, minimum, mean and standard deviation.
The evaluation targets are set to the ten points from (1) to (10) in Fig. 5. As in a previous study (Nagai, M., et al.
2009), the elevation difference between measurement point by ground-based laser scanner and laser unit for UAV
under 0.05 m is evaluated as A, more than 0.05 m and under 0.10 m as B, more than 0.10 m and under 0.15 m as C,
and 0.15 m or more as D, with reference to the precision confirmation standard of the groundwork survey and theworkpiece measurement specified in the Guidelines for Work Progress Management (Ministry of Land, Infrastructure,
Transport and Tourism. 2017a).
However, the ranges of these evaluation targets are not necessarily horizontal planes. Therefore, using the point clouddata of the most probable values, the unevenness of the evaluation range is evaluated by the standard deviation in theelevation direction at the same time as the evaluation of the difference in elevation mentioned above.
5.3. Evaluation results and discussion
Fig. 6 shows the point cloud data with the most probable value obtained by the ground-based laser scanner, andFig. 7 shows the point cloud data obtained by the laser scanner units for UAV. The point cloud data from the UAV-

Copyright: © SANKEI DIGITAL INC.

5

Development of Laser Scanner Units for UAV
Tanaka, S., Imai, R., Nakamura, K., Yamamoto, Y., Tsukada, Y. and Nakahara, M.

Flight route
(2)

(4)

10m

(1)

(3)

(9)

(7)

(5)
(6)

(8)

Extract
pointsinsideframe(10)VerticalGreen point：Measurement point by ground-based laser scannerWhite point：Meaurement point by laser scanner unit for UAV

5cm
Horizontal

Elevation

VerticalMeasurement result of laser scanner unit for UAV(Visualization by color-coding for each route during constant-speed flight)

Horizontal

Vertical

Fig.5. Evaluation method
28,545,448 points

623,048 points
Undergrowth

Fence

Car
VerticalVertical

Car

Fence

Horizontal

Horizontal

Fig.7. Point cloud data by

Fig.6. Point cloud data bylaser scanner unit for UAV

ground-based laser scanner

mounted laser scanner was visualized by color-coding for each flight route filtered by the route profiling function.
The point cloud data indicates that both of them were able to measure fences and vehicles within the measurement
range.
Table 2 presents the evaluation results, and Fig. 8 shows an example of elevation points (1). The standard deviationof the most probable value shows that there is a standard deviation of 0.01 –0.03 m in Table 2. The target field hassome areas with thick undergrowth. Therefore, not only the thickness of the ground surface but also the height of theundergrowth is generated in Fig. 8.
When we check the scores from Grade A to D, we can see that more than 80% to 90% of the scores are A and B
evaluations. When we check the average difference in elevation, we can see that they are all within 0.03m. Consideringthat the measuring precision of LiDAR shown in Table 1 is 0.03m in standard deviation, it is considered that themeasurement is successfully made with the measuring precision of the device.
On the other hand, the standard deviation of the difference in elevation is within the range of 0.02 m to 0.06 m. The
LiDAR measurement points have errors not only in the elevation direction but also in the horizontal direction. If thestandard deviation of the most probable value is also taken into account, it is estimated that the unevenness of theground surface and undergrowth at the location where the displacement occurred affects the standard deviation of thedifference in elevation. Considering the below, it can be inferred that the difference in elevation between the mostprobable value and the point cloud data obtained using the laser scanner unit is successfully measured within the rangeof the measuring precision of the devices. Considering that the standard deviation of the height difference was in therange of 0.11–0.12 m in the previous study, it is considered that the proposed technology improved the accuracy by
0.05 m or more.

Copyright: © SANKEI DIGITAL INC.

6

Development of Laser Scanner Units for UAV
Tanaka, S., Imai, R., Nakamura, K., Yamamoto, Y., Tsukada, Y. and Nakahara, M.

maxThickness ofundergrowth and ground

・・・

min

Elevationdifference

Elevation

Green point：Measurement point by ground-based laser scannerWhite point：Meaurement point by laser scanner unit for UAV

Vertical

Fig.8. Example of elevation points (1)Table 2. Evaluation results
Elevation difference of point cloud data between most probable value and laser scanner unit for UAV
Most probableEvaluation

Grade

Grade

Grade

Grade

value standard
PointsA

B

CStandard
Total points

Max

Min

Average(Points）

(m)

(m)

(m)

D

deviation(m)(Points)(Points)(Points)(Points)

deviation
（m）

(1)

0.03

144

66

2

0

212

0.10

-0.14

-0.02

0.04

(2)

0.02

112

16

0

0

128

0.03

-0.09

-0.03

0.03

(3)

0.03

56

48

10

0

114

0.11

-0.13

-0.01

0.06

(4)

0.02

118

92

16

0

226

0.09

-0.14

-0.03

0.05

(5)

0.02

108

18

0

0

126

0.06

-0.07

-0.00

0.03

(6)

0.02

134

52

8

0

194

0.10

-0.14

-0.00

0.05

(7)

0.02

104

58

2

0

164

0.10

-0.09

-0.01

0.05

(8)

0.02

100

42

24

2

168

0.10

-0.16

-0.04

0.05

(9)

0.01

224

8

0

0

232

0.05

-0.03

-0.01

0.02

(10)

0.03

136

60

20

0

216

0.13

-0.12

-0.01

0.06

Grade A : 0m to less 0.05m, B : 0.05m to less 0.10m, C : 0.10m to less 0.15m, D : more than 0.15m

From these points, it was confirmed that the calibration function of the laser scanner unit allows the measurement
errors of the GNSS receiver and IMU to be corrected, and that the measurement is possible within the range ofmeasurement error of LiDAR only. In addition, it was also confirmed that the measurement errors caused byacceleration/deceleration of the UAV and measurement errors due to the mixture of measurement data from distant
flight routes were also eliminated by the functions of flight condition determination and route profiling. Consequently,
it was confirmed that the laser scanner unit is capable of solving the three problems of the existing UAV -mounted
laser scanner and the existing method (Nagai, M., et al. 2009, Wallace, L., 2012, Martin, C., 2017).
6. Conclusion
In this paper, based on the problems of the existing laser scanner units and the existing method (Nagai, M., et al. 2009,
Wallace, L., 2012, Martin, C., 2017) identified in the existing study (Tanaka, S., et al. 2019), we developed the laser
scanner unis for UAV and new method of generating point cloud data within the laser scanner units to solve theproblems. Then, from the results of the verification experiments, we confirmed the usefulness of the developed laser
scanner units and the method within laser scanner units. However, since the precision prescribed in the Guidelines forWork Progress Management (Ministry of Land, Infrastructure, Transport and Tourism. 2017a) is within the range of

Copyright: © SANKEI DIGITAL INC.

7

Development of Laser Scanner Units for UAV
Tanaka, S., Imai, R., Nakamura, K., Yamamoto, Y., Tsukada, Y. and Nakahara, M.
0.02m in the difference in elevation, 0.04m in mechanical surveying, and 0.06m in piecework measurement, further
improvement in precision is necessary. Since the remaining error in this paper is the measurement error of LiDAR, it
is necessary to examine a method of reducing the error of LiDAR. To do so, further improvement in precision can be
expected by applying not only the results of attitude measurement by IMU and the GNSS receiver but also combining
LiDAR, which is utilized in the robotics community, and SLAM, which is a self -position estimation method using
cameras, to correct the errors. In the future, we plan to develop laser scanner units those also combines SLAM (Li,R., 2014) to make them more applicable to actual sites. This paper was translated and written based on the technicalreport published by the Image Lab (Kubota, S., et al. 2023).
Author Contributions
Conceptualization, S.T.; methodology, Y.Y., Y.T. and M.N.; software, M.N.; validation, M.N.; formal analysis, Y.Y.,
Y.T. and M.N.; investigation, Y.Y., Y.T. and M.N.; resources, R.I. and K.N.; data curation, M.N.; writing —originaldraft preparation, Y.Y., Y.T. and M.N.; writing—review and editing, R.I. and K.N.; visualization, M.N.; supervision,
S.T.; project administration, R.I. and K.N.; funding acquisition, R.I. and K.N.;
Funding
This research was funded by Asia Air Survey Co., Lid., FORUM 8 Co., Ltd., Intelligent Style Co., Ltd., Mitsubishi
Electric Engineering Co., Ltd., PASCO Corp. and ZENRIN CO., LTD.
Acknowledgments
This study was partially performed under Kansai University Kaiser Project S "Consortium for the Development ofAdvanced Spatial Measurement Technology" and the Ministry of Land, Infrastructure, Transport and Tourism's Grant
for Research and Development of Construction Technology "Research and Development on Utilization of
Measurement Data Using UAV with Laser Scanner for Construction Management of River Earthworks". We would
like to express our gratitude for their support.
Conflicts of Interest
The authors declare no conflict of interest.
References
Elamin, A. (2022). A GNSS/INS/LiDAR Integration Scheme for UAV-Based Navigation in GNSS-Challenging
Environments. Sensors, 22(24), 1-15.
Kubota, S., et al.(2023). Development of UAV-mounted Laser Scanner Unit and Point Cloud Data Generation Method.
Image Lab, 34(2), 19-25.
Li, R., et al.(2014). LiDAR/MEMS IMU Integrated Navigation (SLAM) Method for a Small Uav in Indoor
Environments. Inertial Sensors and Systems Symposium 2014, 1(1), 1-15.
Martin, C., et al.(2017). Designing and Testing a UAV Mapping System for Agricultural Field Surveying . Sensors,
17(12), 2703-2722.
Ministry of Land, Infrastructure, Transport and Tourism. (2017a). Guidelines for Work Progress Management
using Unmanned Aerial Vehicle-mounted Laser Scanner (Earthworks) (Draft). http://www.hrr.mlit.go.jp/gij
yutu/i_Construction/youryou_kijun/11kisaikensa/11_9_ koukuureza_dekidaka_doko.pdf.
Ministry of Land, Infrastructure, Transport and Tourism. (2017b). Public Survey Manual using UAV (Draft).
https://www.gsi.go.jp/common/000186712.pdf.
Nagai, M., et al.(2009). UAV-borne 3-D Mapping System by Multisensor Integration. Transactions on Geoscienceand Remote Sensing, 47(3), 701-708.
Sofonia, J., et al.(2019). Observing Geomorphological Change on an Evolving Coastal Sand Dune Using SLAM Based UAV LiDAR. Remote Sensing in Earth Systems Sciences, 25(2), 273-291.
Tanaka, S., et al.(2019). A Study on Measurement Method and Measurement Error using UAV-mounted Laser Scanner
for Construction Management of Earthworks. Journal of Japan Society of Civil Engineers, Ser. F3 Civil
Engineering Informatics, 74(2), l_125- l_135.
Wallace, L., et al.(2012). Development of a UAV-LiDAR System with Application to Forest Inventory,
Photogrammetry and Remote Sensing, 4(6), 1519-1543.

Copyright: © SANKEI DIGITAL INC.

8