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Multirotor UAV-Based Photogrammetric Mapping for Road Design

Multirotor UAV-Based Photogrammetric Mapping for Road Design Hindawi International Journal of Optics Volume 2018, Article ID 1871058, 7 pages https://doi.org/10.1155/2018/1871058 Research Article Multirotor UAV-Based Photogrammetric Mapping for Road Design Muhammad Akmal Zulkipli and Khairul Nizam Tahar Centre of Studies for Surveying Science and Geomatics, Faculty of Architecture, Planning, and Surveying, Universiti Teknologi MARA, 40450 Shah Alam, Selangor Darul Ehsan, Malaysia Correspondence should be addressed to Khairul Nizam Tahar; nizamtahar@gmail.com Received 18 June 2018; Revised 6 August 2018; Accepted 13 September 2018; Published 1 October 2018 Guest Editor: Wei Liu Copyright © 2018 Muhammad Akmal Zulkipli and Khairul Nizam Tahar. is Th is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Unmanned Aerial Vehicles (UAVs) can be used for close range mapping. In engineering survey works, the conventional survey involves huge cost, labour, and time. Low-cost UAVs are very practical in providing reliable information for many applications such as road design. UAVs can provide the output that meets the accuracy of engineering surveys and policies, especially for small-scale mapping. UAVs are also a competitive technology which is stable and rapidly developing, same as other surveying technologies. is Th study investigates the performance of multirotor UAV for road design. This study involves four phases which consist of preliminary study, data collection, data processing, and analysis. is Th study focuses on the UAV as a tool to capture data of the ground from a certain altitude. The analysis includes UAV flight planning, image acquisition, and accuracy assessment of road design. It can be concluded that UAVs can be used to provide data for road design with reliable accuracy. 1. Introduction imagery, radar, LiDAR, and land survey. The Ground Sam- pling Distance (GSD) is the most important characteristic Developed countries are constantly faced with high main- to be considered during road design. The other information tenance cost of aging transportation highways. The growth such as elevation and positioning also influences the road of the motor vehicle industry and accompanying economic design. growth has generated a demand for safer, better perform- Previously, topography information especially in road ing, less congested highways [1]. The growth of commerce, design was obtained from land survey using total station. educational institutions, housing, and defense has largely The land survey method required a lot of time to complete drawn from government budgets in the past, making the the survey especially for large area. Therefore, this method financing of public highways a challenge. The multipurpose also increased the cost of the project and labour used to characteristics of highways, economic environment, and complete the project. The land survey method totally relies on the advances in highway pricing technology are constantly human resource in order to carry out road design. Therefore, changing. Therefore, the approaches to highway financing, the road design is prone to the systematic error done by management, and maintenance are constantly changing as human. The undulated area is a challenge for land survey well. Management of safety is a systematic process that strives method because it requires the human to climb up and to reduce the occurrence and severity of traffic accidents. down and access the challenging site on their own. The other The man/machine interaction with road traffic systems is factor is weather conditions and land ownership issue during unstable and poses a challenge to highway safety man- conducting the land survey. The long road alignment might agement [2]. High accuracy of topographical information cause alotoferrorsand it mighthave problem to merge and features information are very important in good road the data at the end [4]. The other method used to capture alignment design [3]. There are many previous methods used topography information is Light Detection and Ranging to capture topography information such as manned satellite (LiDAR). This method oeff rs the accurate topographic data 2 International Journal of Optics for road design application. LiDAR method can cover the 2. Methodology large area in a minutes and could provide the accurate data. This study involves four phases which include preliminary LiDAR is also an active sensor which is capable of capturing study and planning, data collection, data processing, results, data the day or night. However, this method is very expensive and analysis. The methodology plays an important role in which can aec ff t the total project cost. Satellite imagery also implementing this study accordingly. The first phase is on can be used to obtain topographic information for road the preliminary study and the planning of the work which design application. The satellite imageries were captured from are crucial parts of the study that require a lot of reading thousand kilometres from the earth surface [5]. This method and planning. The rfi st phase also includes reconnaissance, could provide the location and classification of topographic calibration of the equipment, and measurement of data. The information. This method also has its own problem due to next phase is on data collection using UAV images and getting the revisit time of the satellite at the same location, weather the detailed plans from the unit facilities of UiTM Puncak condition which refer to cloudy condition, and resolution of Alam. Then, both sets of data are processed in the laboratory the satellite images. for the third phase of this study which is data processing. The The key for increasing the safety of highway systems is UAV images are processed using the UAV Agisoft PhotoScan to design, build, and maintain them to be far more tolerant software which can generate the X, Y, and Z coordinates of the of theaverage rangeofthis man or machineinteraction with proposed road. This phase also includes the construction of highways. Technological advancements in highway engineer- road mapping of the area of interest. After the data processing ing have improved the design, construction, and maintenance is completed, the results are analysed. The flowchart of the methods used over the years. These advancements have research methodology is shown in Figure 1. allowed for newer highway safety innovations. By ensuring Reconnaissance survey is a general study of an area of that all situations and opportunities are identified, consid- interest that might be used for a road or airfield. The recon- ered, and implemented as appropriate, they can be evaluated naissance survey report should summarise all the collected in every phase of highway planning, design, construction, information, including a description of the site, a conclusion maintenance, and operation to increase the safety of our on the economy of its use,and,whereverpossible, maps.For highway systems. UAV surveys, the important thing is the area of coverage The new technology such as UAV photogrammetry of the site. Furthermore, the reconnaissance must include opens numerous applications in the close range domain, geographical features for the decision-making of minimum combining aerial and terrestrial photogrammetry but also yfl ing height, direction of flight path, and type of UAV used. introduces low-cost alternatives to the conventional manned Other than that, this survey also needs to consider the wind aerial photogrammetry [6–9]. UAV systems also offer various direction tomake sure that itiscarried outsmoothly and also applications other than mapping such as surveillance, arche- for the direction of the yfl ing path to obtain better image cap- ological, geohazard studies, monitoring, and fire disaster [10– ture. Figure 2 shows the location of study. Camera calibration 22]. The UAV system is equipped with various intelligent is carried out before acquiring the digital aerial images or sensors such as barometer, Inertial Navigation System (INS), aerial photographs for the purpose of recovering all camera Global Positioning System (GPS), flight control, navigation parameters for digital image processing. This study used a control, sonar, and infrared and Electronic Speed Controller 4K camera with 4000 x 3000 resolution and the process of (ESC) [23–27]. There are many kinds of UAVs available camera calibration also gives a result of focal length, principal in the market which are aor ff dable. UAV photogrammetry points, radial lens distortion, and tangential lens distortions. describes photogrammetric measurement platforms which These camera parameters need to be included during the operate as either remotely controlled, semiautonomously, or interior orientation step in digital image processing autonomously. The definition covers balloons, kites, gliders, The UAV used in this study is the Quad-Rotors, Phantom airships, rotary, and xe fi d wing UAVs with the capability 3 Professional. The UAV weight (including battery and for photogrammetric data acquisition in manual, semiauto- propellers) is about 1280 g and the diagonal size (excluding mated, and automated flight modes [28]. Model helicopters propellers) is 350 mm. Its maximum speed can achieve 16 are able to operate closer to the object and are highly flexible m/s (ATTI mode, no wind) and has a GPS Mode that uses in navigation compared to fixed wing UAVs. Microdrones are GLONASS satellite. The UAV maximum flight time is approx- more stable against environmental conditions such as wind. imately about 23 minutes. A single person can operate this The developments of model helicopters and comparable UAV to collect the image data. The hardware uses IOS Ipad autonomous vehicles are primarily driven by the artificial to control the flight planning and a computer for processing. intelligence. Nowadays, close range areas can be mapped The detailed plan is acquired from the unit facilities in the by combining aerial and terrestrial photogrammetry using DWG lfi e in AutoCAD and it can be georeferenced using UAV technologies and also as an alternative way for large the ArcGIS software. After that, the acquired UAV images area mapping [29]. Low cost UAVs are used in mapping were processed using the Agisoft PhotoScan sow ft are. Next, projects with low budgets. However, in the previous years, the outputs are transferred to the AutoCAD and the ArcGIS low-cost UAVs have reached a level of practical reliability sow ft are to analyse the outputs. and professionalism which allow the use of these systems such as mapping platforms. In study, the multirotor UAV is used to capture the topographic information for road design 2.1. Data Acquisition and Flight Planning. Flight planning application. involves the control of dimension of the study area, number International Journal of Optics 3 Preliminary Study Phase 1 Reconnaissance UAV Flight Planning Calibration Acquire Digital Images Acquire Detail Plan Phase 2 Establish GCPs GCPs and CPs Processing Georeferencing Phase 3 Image Processing Orthophoto Road Mapping Road Design (alternative Phase 4 Accuracy Assessment road) Quantitative Figure 1: Research methodology. Figure 2: Location of study area. Figure 3: Flight planning for acquisition of data. of strips required, pixel size, photo scale yin fl g height, and endlap, sidelap, and wind direction are as shown in Figure 3. percentage of the end lap and side lap. The Map Pilot used is After parameters setting of the flight planning is completed a friendly software for the drone, module Waypoint Editor, in the Map Pilot, the flight planning is uploaded to the UAV. and it is able to design flight planning. There are about After uploading, the start button is pressed to begin the image 122 number of images with 148 m yin fl g height created acquisition. Once it started, the UAV gave information of in the flight planning. In general, the aerial photographs the current altitude of the mission. After the UAV entered should be overlapped at least 80% and the side for at least the survey area, images are automatically captured every 2.5 60%. This requirement needs to be fulfilled to make sure seconds and identical speed is used to ensure accurate data. that quality photogrammetry results could be obtained. The Once the mapping is completed, the UAV returned to the customised parameters such as spatial resolution, altitude, takeoff location and landed automatically. 4 International Journal of Optics Table 1: Coordinates of road design. Conventional Road Design UAV Images road Design CPs X (m) Y (m) Z(m) X (m) Y (m) Z(m) CP -26926.806 3109.908 42.124 -26926.435 3110.456 41.431 CP -26847.403 3128.347 44.154 -26847.554 3128.184 44.310 CP -26715.708 3158.090 39.246 -26716.024 3157.619 38.570 CP -26813.002 3178.296 41.423 -26813.127 3178.034 41.133 CP -27024.207 3186.877 43.806 -27024.254 3186.939 44.205 CP -26967.798 3240.365 43.403 -26967.768 3240.422 43.903 CP -27064.443 3216.702 43.206 -27064.472 3216.801 43.322 CP -27167.600 3078.530 44.500 -27167.648 3078.670 44.406 CP -27188.670 2957.179 44.720 -27188.601 2957.334 45.069 CP -27182.072 2969.390 44.407 -27181.878 2969.580 45.146 CP -27078.698 2825.722 48.370 -27078.685 2825.727 48.610 CP -26879.317 2724.228 48.912 -26879.289 2724.209 48.114 CP -26860.444 2769.504 49.188 -26860.493 2769.433 48.635 CP -26851.342 2876.084 45.039 -26851.430 2875.994 44.772 establish the GCP on the UAV images is selected by importing the coordinates in the (txt) file. The coordinate is illustrated by point features in the software, where it needs to move the point to the exact location of the GCP. The process will produce Orthophoto and a map. 2.3. Data Analysis and Assessment. The objective of this study is to assess the accuracy of road mapping from the UAV product. There are two assessments describing the point and visual analysis. For quantitative accuracy, the error difference Figure 4: Location of GCPs and CPs. between the coordinates of check points by GPS (rapid static technique) and UAV processed images data is assessed using RMSE. After the accuracy assessment has been conducted, 2.2. Preprocessing and Processing. The ground control points an alternative road can be designed using the UAV data (GCPs) and check points (CPs) are collected using GPS andgroundsurveymapping of the study area. The designed observation through the rapid static technique. This tech- road is focused on increasing the ecffi iency of road o fl w. The nique can determine the position information which includes optimal road alignment is determined based on topographic Northing, Easting, and Elevation (X, Y, and Z) through map of the area and mapping the existing road network. There postprocessing by using the GNSS Solution software. This are two types of alignments which are vertical and horizontal software can convert the raw data to a Rinex lfi e, where it alignment. The horizontal and vertical alignments based on can be used in any GPS processing software for adjustments. topographic features from the UAV data are determined. The rapid static technique only takes 5 to 20 minutes for observation. In this study, there are six GCPs established for absolute orientation and fourteen CPs. For a better view, 3. Results and Analysis Figure 4 shows all the locations of the GCPs and CPs. After data acquisition has been completed using the The analysis of the accuracy of the road mapping, production multirotor UAV, all acquired raw images data and GCPs are of the road map and all the results will be analysed in this processed using the Agisoft PhotoScan sow ft are. The results chapter, including the evaluation of UAV images for road are presented in the form of a digital map or hardcopy. design or alternative road. All calculations are made for the The Agisoft PhotoScan sowa ft re requires camera information RMSE point accuracy assessments and the road design. The such as pixel size, focal length, radial lens distortion, and accuracy is analysed based on computation of the RMSE tangential distortion to carry out the interior orientation. between the coordinates of 3D stereo model and the check A total of six GCPs have been established during absolute points. The locations of the check points are shown in orientation. After adding the raw images data in the Agisoft Figure 4 with 14 samples (Table 1). It shows the comparisons PhotoScan software, the process will start with aligning between CPs using GPS and the 3D coordinates of the stereo the photo input in a high accuracy setting and using the model in Agisoft Photoscan sow ft are. Reference for Pair Preselection. The key and tie point limit are It can be seen that the accuracy could be achieved 40,000 and 20,000. Aeft r aligning the photos, a reference to using the UAV system. The smaller the value of the RMSE International Journal of Optics 5 Figure 5: Digitized features from UAV images. Figure 7: Complete road designs for UAV Images and Conventional Survey. with 50 and 60 m radius. This radius follows the public work department standards for Exclusive Motorcycle Lane, EML. 4. Discussions After producing the adjustment coordinates, the WGS84 coordinate system is converted to a local coordinate system from angular units to meters. The coordinate system that is chosen is Cassini Malaysia, Selangor, by using Kertau as a datum. The reason that the coordinate system is converted to Cassini Malaysia is because it is easy to analyse the meter units than the angular degree minutes and seconds, where it Figure 6: Digital surface models of UAV images data. can check the miss-closure (meter) of the data. The RMSE for x, y, and z coordinates are ± 0.155 metres, ± 0.228 metres, and ± 0.479 metres, respectively (Table 2). In the Global Mapper sowa ft re, digitising of features are calculated, the higher the accuracy. Hence, the accuracy carried out using orthophoto image and then exported to of the orthophoto can be calculated by the RMSE value. ArcGIS in order to produce a map. The analysis only focused The latitude, longitude, and elevation (X, Y, and Z) are on road features and it could be displayed in ArcGIS to processed using the GNSS Solution software where the raw visualise the differences between UAV road mapping and data collected from the site are converted to (Rinex) file to be conventional road mapping. The AutoCAD drawings are used in any GPS processing software for adjustments. After obtained from the unit facilities of UiTM Puncak Alam. processing the raw data of the UAV images in the Agisoft Photoscan software, the outcome of orthomosaic in a (tif) lfi e Figure 8 shows the slight difference between two methods of producing the map; the road with the pink outline is will be imported to the Global Mapper sow ft are in order to drawn using the AutoCAD software which is obtained from carry out the digitisation of the features as shown in Figure 5. The analyses are carried out based on road features and conventional ground measurement while the grey outline is digitised from the UAV’s orthophoto in the Global Mapper the elevation produced by the Digital Surface Model (DSM) sow ft are. (Figure 6). The digitised features are displayed in ArcGIS to visualise the difference from the AutoCAD drawing. After both road designs are completed, both UAV images 5. Conclusions and Recommendations andconventional roaddesign are compared with the coor- dinate of chainage in every 30 meter intervals. In the CDS Road mapping is produced from the UAV images using the software, parameters of the road are needed to design the road Agisoft Photoscan software to create an orthophoto image. curve such as bearing in, bearing out, and the optimum radius All the images went through the scaling and level process for the road curve. Two road curves have been designed which also referred to the orientations such as interior, with the radius of 50 and 60 meters for the first and second relative, and exterior orientation. It is demonstrated that the curve. After clicking the position of the Intersection Point UAV together with the digital camera are capable of acquiring and keying in the value of radius, bearing in and bearing out aerial photograph successfully for large scale mapping in a the sow ft are will calculate the other parameters values which short amount of time. This study shows that UAV is also are available for the designed road curve such as arc, chord, capable of producing road mapping at the selected study and tangent length. Figure 7 shows the finished road design area. All of the results are analysed using the Root Mean 6 International Journal of Optics Table 2: Analysis on disparity between conventional and UAV coordinates. CPs X(m) Y(m) Z(m) X (m) Y (m) Z (m) CP -0.371 -0.548 -0.693 0.138 0.300 0.480 CP 0.151 0.163 -0.156 0.023 0.027 0.024 CP 0.316 0.471 0.676 0.100 0.222 0.457 CP 0.125 0.262 0.290 0.016 0.069 0.084 CP 0.047 -0.062 -0.399 0.002 0.004 0.159 CP -0.030 -0.057 -0.500 0.001 0.003 0.250 CP 0.029 -0.099 -0.116 0.001 0.010 0.013 CP 0.048 -0.140 0.094 0.002 0.020 0.009 CP -0.069 -0.155 -0.349 0.005 0.024 0.122 CP -0.194 -0.190 -0.739 0.038 0.036 0.546 CP -0.013 -0.005 -0.240 0.000 0.000 0.058 CP -0.028 0.019 0.798 0.001 0.000 0.637 CP 0.049 0.071 0.553 0.002 0.005 0.306 CP 0.088 0.090 0.267 0.008 0.008 0.071 RMSE (m) 0.155 0.228 0.479 get more details for the topographical map to design a road. Different methods of camera calibration could be applied to optimise the quality of the UAV image processing. MyRTKnet data should be used as a base for better control of the coordinate GCP to apply on the UAV images for processing. Dieff rent flying heights should be applied for UAV to conduct better results for road design. Data Availability The data used to support the findings of this study are available from the corresponding author upon request. Figure 8: Visual Accuracy Assessments of Both Road Designs. Conflicts of Interest The authors declare that they have no conflicts of interest. Square Error (RMSE). RMSE can assess the accuracy of the Acknowledgments UAV images using the check points that have been measured on the ground for data validation analysis. The designed Faculty of Architecture, Planning, and Surveying Universiti road in this study is for proposing an alternative road to Teknologi MARA (UiTM), Research Management Institute increase the efficiency of road traffic flow. The Digital Terrain (RMi), and Ministry of Higher Education (MOHE) are Model (DTM) data from the UAV images processed is used greatly acknowledged for providing the fund BESTARI 600- to evaluate the geographic features where it must follow the IRMI/MyRA 5/3/BESTARI (001/2017) to enable this research design policy parameters in order to help control the design to be carried out. The authors would also like to thank of the road. The safety of the users must also be calculated by the people who were directly or indirectly involved in this selecting the super elevation of the proposed road. Therefore, research. the alternative road can be designed using the parameters for road design provided by public work department standards. References In the future, the accuracy of the orthophoto can be improved and enhanced by increasing the number of GCPs and CPs [1] Z. Jaal and J. Abdullah, “User’s Preferences of Highway Land- during data collection in the field using the GPS technique. scapes in Malaysia: A Review and Analysis of the Literature,” It can also minimise the RMSE in data processing. The use Procedia - Social and Behavioral Sciences,vol. 36, pp.265–272, of DTM (digital terrain model) for road design to check the accuracy of the road designed and to improve the validation [2] S. Mart´ınez, J. Ortiz, and M. L. Gil, “Geometric documentation of using UAV for road design construction. Different types of of historical pavements using automated digital photogram- UAV shouldbe usedto improve the efl xibility of the survey metry and high-density reconstruction algorithms,” Journal of works; for example, a fixed wing UAV can cover a large area to Archaeological Science,vol.53,pp.1–11, 2015. International Journal of Optics 7 [3] M. M. S. Albattah, “Optimum Highway Design and Site Loca- [20] K. N. Tahar and A. Ahmad, “Production of Orthophoto and tion Using Spatial Geoinformatics Engineering,” Journal of Volume Determination Using Low-Cost Digital Cameras,” Per- Remote Sensing & GIS,vol.5,no. 1,p.10,2016. tanika Journal of Science and Technology, vol.21,no. 2,pp.387– 396, 2011. [4] P.Kettunen,C.Koski, andJ.Oksanen,“Adesign ofcontourgen- [21] P. Xiaodong and L. Zongjian, “Unmanned airship low altitude eration for topographic maps with adaptive DEM smoothing,” International Journal of Cartography,vol.3,no.1,pp.19–30,2017. system for aerial photogrammetry,” Science of Surveying and Mapping, vol.34,no.4,pp.33–35, 2009. [5] F. van Coillie, K. Delaplace, D. Gabriels et al., “Monotempo- [22] X.Du,X.Jin,X.Zhang, J.Shen,andX.Hou,“Geometry features ral assessment of the population structure of Acacia tortilis measurement of traffic accident for reconstruction based on (Forssk.) Hayne ssp. raddiana (Savi) Brenan in Bou Hedma close-ra nge photogrammetry,” Advances in Engineering Soft- National Park, Tunisia: A terrestrial and remote sensing ware, vol.40, no.7,pp.497–505,2009. approach,” Journal of Arid Environments,vol. 129,pp. 80–92, 2016. [23] J. Chahl, “rTh ee biomimetic flight control sensors,” Interna- tional Journal of Intelligent Unmanned Systems,vol.2,no. 1,pp. [6] H. Eisenbeiss, UAV Photogrammetry, DISS. ETH NO. 18515,1- 27–39, 2014. 237, 2009. [24] H. Chao, Y. Cao, and Y. Chen, “Autopilots for small unmanned [7] Y. Fujii, E. Fodde, K. Watanabe, and K. Murakami, “Digital pho- aerial vehicles: A survey,” International Journal of Control, togrammetry for the documentation of structural damage in Automation, and Systems, vol.8,no.1,pp. 36–44, 2010. earthen archaeological sites: the case of Ajina Tepa, Tajikistan,” [25] F. Delgado, J. Carvalho, T. Coelho, and A. Dos Santos, “An Engineering Geology,vol.105, no.1-2,pp.124–133,2009. Optical Fiber Sensor and Its Application in UAVs for Current [8] Z. Lin, “UAV aiborne low altitude photogrammetry system,” Measurements,” Sensors, vol. 16, no. 11, p. 1800, 2016. Science of Surveying and Mapping, vol.36,no. 1, p.5,2011. [26] S. Jabari, F. Fathollahi, A. Roshan, and Y. Zhang, “Improving [9] M.A.Nu˜ ´nez, F. Buill, and M. Edo, “3D model of the Can UAV imaging quality by optical sensor fusion: an initial study,” Sadurn´ıcave,” Journal of Archaeological Science, vol.40, no.12, International Journal of Remote Sensing, vol.38, no.17,pp.4931– pp.4420–4428,2013. 4953, 2017. [10] H. Bendea, F. Chiabrando, F. Tonolo, and D. Marenchino, “Map- [27] A. B. Junaid, Y. Lee, and Y. Kim, “Design and implementation ping of archaeological areas using a low-cost UAV the Augusta of autonomous wireless charging station for rotary-wing UAVs,” Bagiennorum Test,” XXI International CIPA Symposium,pp. 1– Aerospace Science and Technology, vol.54,pp.253–266, 2016. 8, 2007. [28] H. Eisenbeiss, Photogrammetric Week ’11, F. Dieter, Ed., 2011, [11] I. Colomina and P. Molina, “Unmanned aerial systems for Wichmann/VDE Verlag, Belin & Offenbach. photogrammetry and remote sensing: a review,” ISPRS Journal [29] K. N. Tahar and A. Ahmad, “A simulation study on the capa- of Photogrammetry and Remote Sensing, vol.92, pp.79–97,2014. bilities of rotor wing unmanned aerial vehicle in aerial terrain [12] G. J. Grenzdor ¨ eff r, M. Guretzki, and I. Friedlander, “Pho- mapping,” International Journal of Physical Sciences,vol.7,no. togrammetric image acquisition and image analysis of oblique 8, 2012. imagery,” eTh Photogrammetric Record , vol.23,no.124,pp. 372– 386, 2008. [13] R. N. Jiang and D. V. Jauregui, “Development of a digital close- range photogrammetric bridge deflection measurement sys- tem,” Measurement,vol.43,no.10,pp.1431–1438, 2010. [14] T. Luhmann, “Close range photogrammetry for industrial applications,” ISPRS Journal of Photogrammetry and Remote Sensing, vol.65, no. 6,pp. 558–569,2010. [15] K. Konolige and M. Agrawal, “FrameSLAM: From bundle adjustment to real-time visual mapping,” IEEE Transactions on Robotics,vol.24,no.5,pp.1066–1077, 2008. [16] S. G. Kontogiannis and J. A. Ekaterinaris, “Design, performance evaluation and optimization of a UAV,” Aerospace Science and Technology, vol.29,no.1,pp.339–350,2013. [17] M. Rod ¨ er, H. Latifi, S. Hill et al., “Application of optical unmanned aerial vehicle-based imagery for the inventory of natural regeneration and standing deadwood in post-disturbed spruce forests,” International Journal of Remote Sensing,pp. 1– 22, 2018. [18] L. Sahar, S. Muthukumar, and S. P. French, “Using aerial imagery and gis in automated building footprint extraction and shape recognition for earthquake risk assessment of urban invento- ries,” IEEE Transactions on Geoscience and Remote Sensing,vol. 48, no. 9, pp. 3511–3520, 2010. [19] S. Pal Singh, K. Jain, and V. Mandla, “Virtual 3D Campus Mod- eling by Using Close Range Photogrammetry,” American Jour- nal of Civil Engineering and Architecture, vol.1,no.6,pp. 200– 205, 2013. 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Multirotor UAV-Based Photogrammetric Mapping for Road Design

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Hindawi Publishing Corporation
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Copyright © 2018 Muhammad Akmal Zulkipli and Khairul Nizam Tahar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

Hindawi International Journal of Optics Volume 2018, Article ID 1871058, 7 pages https://doi.org/10.1155/2018/1871058 Research Article Multirotor UAV-Based Photogrammetric Mapping for Road Design Muhammad Akmal Zulkipli and Khairul Nizam Tahar Centre of Studies for Surveying Science and Geomatics, Faculty of Architecture, Planning, and Surveying, Universiti Teknologi MARA, 40450 Shah Alam, Selangor Darul Ehsan, Malaysia Correspondence should be addressed to Khairul Nizam Tahar; nizamtahar@gmail.com Received 18 June 2018; Revised 6 August 2018; Accepted 13 September 2018; Published 1 October 2018 Guest Editor: Wei Liu Copyright © 2018 Muhammad Akmal Zulkipli and Khairul Nizam Tahar. is Th is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Unmanned Aerial Vehicles (UAVs) can be used for close range mapping. In engineering survey works, the conventional survey involves huge cost, labour, and time. Low-cost UAVs are very practical in providing reliable information for many applications such as road design. UAVs can provide the output that meets the accuracy of engineering surveys and policies, especially for small-scale mapping. UAVs are also a competitive technology which is stable and rapidly developing, same as other surveying technologies. is Th study investigates the performance of multirotor UAV for road design. This study involves four phases which consist of preliminary study, data collection, data processing, and analysis. is Th study focuses on the UAV as a tool to capture data of the ground from a certain altitude. The analysis includes UAV flight planning, image acquisition, and accuracy assessment of road design. It can be concluded that UAVs can be used to provide data for road design with reliable accuracy. 1. Introduction imagery, radar, LiDAR, and land survey. The Ground Sam- pling Distance (GSD) is the most important characteristic Developed countries are constantly faced with high main- to be considered during road design. The other information tenance cost of aging transportation highways. The growth such as elevation and positioning also influences the road of the motor vehicle industry and accompanying economic design. growth has generated a demand for safer, better perform- Previously, topography information especially in road ing, less congested highways [1]. The growth of commerce, design was obtained from land survey using total station. educational institutions, housing, and defense has largely The land survey method required a lot of time to complete drawn from government budgets in the past, making the the survey especially for large area. Therefore, this method financing of public highways a challenge. The multipurpose also increased the cost of the project and labour used to characteristics of highways, economic environment, and complete the project. The land survey method totally relies on the advances in highway pricing technology are constantly human resource in order to carry out road design. Therefore, changing. Therefore, the approaches to highway financing, the road design is prone to the systematic error done by management, and maintenance are constantly changing as human. The undulated area is a challenge for land survey well. Management of safety is a systematic process that strives method because it requires the human to climb up and to reduce the occurrence and severity of traffic accidents. down and access the challenging site on their own. The other The man/machine interaction with road traffic systems is factor is weather conditions and land ownership issue during unstable and poses a challenge to highway safety man- conducting the land survey. The long road alignment might agement [2]. High accuracy of topographical information cause alotoferrorsand it mighthave problem to merge and features information are very important in good road the data at the end [4]. The other method used to capture alignment design [3]. There are many previous methods used topography information is Light Detection and Ranging to capture topography information such as manned satellite (LiDAR). This method oeff rs the accurate topographic data 2 International Journal of Optics for road design application. LiDAR method can cover the 2. Methodology large area in a minutes and could provide the accurate data. This study involves four phases which include preliminary LiDAR is also an active sensor which is capable of capturing study and planning, data collection, data processing, results, data the day or night. However, this method is very expensive and analysis. The methodology plays an important role in which can aec ff t the total project cost. Satellite imagery also implementing this study accordingly. The first phase is on can be used to obtain topographic information for road the preliminary study and the planning of the work which design application. The satellite imageries were captured from are crucial parts of the study that require a lot of reading thousand kilometres from the earth surface [5]. This method and planning. The rfi st phase also includes reconnaissance, could provide the location and classification of topographic calibration of the equipment, and measurement of data. The information. This method also has its own problem due to next phase is on data collection using UAV images and getting the revisit time of the satellite at the same location, weather the detailed plans from the unit facilities of UiTM Puncak condition which refer to cloudy condition, and resolution of Alam. Then, both sets of data are processed in the laboratory the satellite images. for the third phase of this study which is data processing. The The key for increasing the safety of highway systems is UAV images are processed using the UAV Agisoft PhotoScan to design, build, and maintain them to be far more tolerant software which can generate the X, Y, and Z coordinates of the of theaverage rangeofthis man or machineinteraction with proposed road. This phase also includes the construction of highways. Technological advancements in highway engineer- road mapping of the area of interest. After the data processing ing have improved the design, construction, and maintenance is completed, the results are analysed. The flowchart of the methods used over the years. These advancements have research methodology is shown in Figure 1. allowed for newer highway safety innovations. By ensuring Reconnaissance survey is a general study of an area of that all situations and opportunities are identified, consid- interest that might be used for a road or airfield. The recon- ered, and implemented as appropriate, they can be evaluated naissance survey report should summarise all the collected in every phase of highway planning, design, construction, information, including a description of the site, a conclusion maintenance, and operation to increase the safety of our on the economy of its use,and,whereverpossible, maps.For highway systems. UAV surveys, the important thing is the area of coverage The new technology such as UAV photogrammetry of the site. Furthermore, the reconnaissance must include opens numerous applications in the close range domain, geographical features for the decision-making of minimum combining aerial and terrestrial photogrammetry but also yfl ing height, direction of flight path, and type of UAV used. introduces low-cost alternatives to the conventional manned Other than that, this survey also needs to consider the wind aerial photogrammetry [6–9]. UAV systems also offer various direction tomake sure that itiscarried outsmoothly and also applications other than mapping such as surveillance, arche- for the direction of the yfl ing path to obtain better image cap- ological, geohazard studies, monitoring, and fire disaster [10– ture. Figure 2 shows the location of study. Camera calibration 22]. The UAV system is equipped with various intelligent is carried out before acquiring the digital aerial images or sensors such as barometer, Inertial Navigation System (INS), aerial photographs for the purpose of recovering all camera Global Positioning System (GPS), flight control, navigation parameters for digital image processing. This study used a control, sonar, and infrared and Electronic Speed Controller 4K camera with 4000 x 3000 resolution and the process of (ESC) [23–27]. There are many kinds of UAVs available camera calibration also gives a result of focal length, principal in the market which are aor ff dable. UAV photogrammetry points, radial lens distortion, and tangential lens distortions. describes photogrammetric measurement platforms which These camera parameters need to be included during the operate as either remotely controlled, semiautonomously, or interior orientation step in digital image processing autonomously. The definition covers balloons, kites, gliders, The UAV used in this study is the Quad-Rotors, Phantom airships, rotary, and xe fi d wing UAVs with the capability 3 Professional. The UAV weight (including battery and for photogrammetric data acquisition in manual, semiauto- propellers) is about 1280 g and the diagonal size (excluding mated, and automated flight modes [28]. Model helicopters propellers) is 350 mm. Its maximum speed can achieve 16 are able to operate closer to the object and are highly flexible m/s (ATTI mode, no wind) and has a GPS Mode that uses in navigation compared to fixed wing UAVs. Microdrones are GLONASS satellite. The UAV maximum flight time is approx- more stable against environmental conditions such as wind. imately about 23 minutes. A single person can operate this The developments of model helicopters and comparable UAV to collect the image data. The hardware uses IOS Ipad autonomous vehicles are primarily driven by the artificial to control the flight planning and a computer for processing. intelligence. Nowadays, close range areas can be mapped The detailed plan is acquired from the unit facilities in the by combining aerial and terrestrial photogrammetry using DWG lfi e in AutoCAD and it can be georeferenced using UAV technologies and also as an alternative way for large the ArcGIS software. After that, the acquired UAV images area mapping [29]. Low cost UAVs are used in mapping were processed using the Agisoft PhotoScan sow ft are. Next, projects with low budgets. However, in the previous years, the outputs are transferred to the AutoCAD and the ArcGIS low-cost UAVs have reached a level of practical reliability sow ft are to analyse the outputs. and professionalism which allow the use of these systems such as mapping platforms. In study, the multirotor UAV is used to capture the topographic information for road design 2.1. Data Acquisition and Flight Planning. Flight planning application. involves the control of dimension of the study area, number International Journal of Optics 3 Preliminary Study Phase 1 Reconnaissance UAV Flight Planning Calibration Acquire Digital Images Acquire Detail Plan Phase 2 Establish GCPs GCPs and CPs Processing Georeferencing Phase 3 Image Processing Orthophoto Road Mapping Road Design (alternative Phase 4 Accuracy Assessment road) Quantitative Figure 1: Research methodology. Figure 2: Location of study area. Figure 3: Flight planning for acquisition of data. of strips required, pixel size, photo scale yin fl g height, and endlap, sidelap, and wind direction are as shown in Figure 3. percentage of the end lap and side lap. The Map Pilot used is After parameters setting of the flight planning is completed a friendly software for the drone, module Waypoint Editor, in the Map Pilot, the flight planning is uploaded to the UAV. and it is able to design flight planning. There are about After uploading, the start button is pressed to begin the image 122 number of images with 148 m yin fl g height created acquisition. Once it started, the UAV gave information of in the flight planning. In general, the aerial photographs the current altitude of the mission. After the UAV entered should be overlapped at least 80% and the side for at least the survey area, images are automatically captured every 2.5 60%. This requirement needs to be fulfilled to make sure seconds and identical speed is used to ensure accurate data. that quality photogrammetry results could be obtained. The Once the mapping is completed, the UAV returned to the customised parameters such as spatial resolution, altitude, takeoff location and landed automatically. 4 International Journal of Optics Table 1: Coordinates of road design. Conventional Road Design UAV Images road Design CPs X (m) Y (m) Z(m) X (m) Y (m) Z(m) CP -26926.806 3109.908 42.124 -26926.435 3110.456 41.431 CP -26847.403 3128.347 44.154 -26847.554 3128.184 44.310 CP -26715.708 3158.090 39.246 -26716.024 3157.619 38.570 CP -26813.002 3178.296 41.423 -26813.127 3178.034 41.133 CP -27024.207 3186.877 43.806 -27024.254 3186.939 44.205 CP -26967.798 3240.365 43.403 -26967.768 3240.422 43.903 CP -27064.443 3216.702 43.206 -27064.472 3216.801 43.322 CP -27167.600 3078.530 44.500 -27167.648 3078.670 44.406 CP -27188.670 2957.179 44.720 -27188.601 2957.334 45.069 CP -27182.072 2969.390 44.407 -27181.878 2969.580 45.146 CP -27078.698 2825.722 48.370 -27078.685 2825.727 48.610 CP -26879.317 2724.228 48.912 -26879.289 2724.209 48.114 CP -26860.444 2769.504 49.188 -26860.493 2769.433 48.635 CP -26851.342 2876.084 45.039 -26851.430 2875.994 44.772 establish the GCP on the UAV images is selected by importing the coordinates in the (txt) file. The coordinate is illustrated by point features in the software, where it needs to move the point to the exact location of the GCP. The process will produce Orthophoto and a map. 2.3. Data Analysis and Assessment. The objective of this study is to assess the accuracy of road mapping from the UAV product. There are two assessments describing the point and visual analysis. For quantitative accuracy, the error difference Figure 4: Location of GCPs and CPs. between the coordinates of check points by GPS (rapid static technique) and UAV processed images data is assessed using RMSE. After the accuracy assessment has been conducted, 2.2. Preprocessing and Processing. The ground control points an alternative road can be designed using the UAV data (GCPs) and check points (CPs) are collected using GPS andgroundsurveymapping of the study area. The designed observation through the rapid static technique. This tech- road is focused on increasing the ecffi iency of road o fl w. The nique can determine the position information which includes optimal road alignment is determined based on topographic Northing, Easting, and Elevation (X, Y, and Z) through map of the area and mapping the existing road network. There postprocessing by using the GNSS Solution software. This are two types of alignments which are vertical and horizontal software can convert the raw data to a Rinex lfi e, where it alignment. The horizontal and vertical alignments based on can be used in any GPS processing software for adjustments. topographic features from the UAV data are determined. The rapid static technique only takes 5 to 20 minutes for observation. In this study, there are six GCPs established for absolute orientation and fourteen CPs. For a better view, 3. Results and Analysis Figure 4 shows all the locations of the GCPs and CPs. After data acquisition has been completed using the The analysis of the accuracy of the road mapping, production multirotor UAV, all acquired raw images data and GCPs are of the road map and all the results will be analysed in this processed using the Agisoft PhotoScan sow ft are. The results chapter, including the evaluation of UAV images for road are presented in the form of a digital map or hardcopy. design or alternative road. All calculations are made for the The Agisoft PhotoScan sowa ft re requires camera information RMSE point accuracy assessments and the road design. The such as pixel size, focal length, radial lens distortion, and accuracy is analysed based on computation of the RMSE tangential distortion to carry out the interior orientation. between the coordinates of 3D stereo model and the check A total of six GCPs have been established during absolute points. The locations of the check points are shown in orientation. After adding the raw images data in the Agisoft Figure 4 with 14 samples (Table 1). It shows the comparisons PhotoScan software, the process will start with aligning between CPs using GPS and the 3D coordinates of the stereo the photo input in a high accuracy setting and using the model in Agisoft Photoscan sow ft are. Reference for Pair Preselection. The key and tie point limit are It can be seen that the accuracy could be achieved 40,000 and 20,000. Aeft r aligning the photos, a reference to using the UAV system. The smaller the value of the RMSE International Journal of Optics 5 Figure 5: Digitized features from UAV images. Figure 7: Complete road designs for UAV Images and Conventional Survey. with 50 and 60 m radius. This radius follows the public work department standards for Exclusive Motorcycle Lane, EML. 4. Discussions After producing the adjustment coordinates, the WGS84 coordinate system is converted to a local coordinate system from angular units to meters. The coordinate system that is chosen is Cassini Malaysia, Selangor, by using Kertau as a datum. The reason that the coordinate system is converted to Cassini Malaysia is because it is easy to analyse the meter units than the angular degree minutes and seconds, where it Figure 6: Digital surface models of UAV images data. can check the miss-closure (meter) of the data. The RMSE for x, y, and z coordinates are ± 0.155 metres, ± 0.228 metres, and ± 0.479 metres, respectively (Table 2). In the Global Mapper sowa ft re, digitising of features are calculated, the higher the accuracy. Hence, the accuracy carried out using orthophoto image and then exported to of the orthophoto can be calculated by the RMSE value. ArcGIS in order to produce a map. The analysis only focused The latitude, longitude, and elevation (X, Y, and Z) are on road features and it could be displayed in ArcGIS to processed using the GNSS Solution software where the raw visualise the differences between UAV road mapping and data collected from the site are converted to (Rinex) file to be conventional road mapping. The AutoCAD drawings are used in any GPS processing software for adjustments. After obtained from the unit facilities of UiTM Puncak Alam. processing the raw data of the UAV images in the Agisoft Photoscan software, the outcome of orthomosaic in a (tif) lfi e Figure 8 shows the slight difference between two methods of producing the map; the road with the pink outline is will be imported to the Global Mapper sow ft are in order to drawn using the AutoCAD software which is obtained from carry out the digitisation of the features as shown in Figure 5. The analyses are carried out based on road features and conventional ground measurement while the grey outline is digitised from the UAV’s orthophoto in the Global Mapper the elevation produced by the Digital Surface Model (DSM) sow ft are. (Figure 6). The digitised features are displayed in ArcGIS to visualise the difference from the AutoCAD drawing. After both road designs are completed, both UAV images 5. Conclusions and Recommendations andconventional roaddesign are compared with the coor- dinate of chainage in every 30 meter intervals. In the CDS Road mapping is produced from the UAV images using the software, parameters of the road are needed to design the road Agisoft Photoscan software to create an orthophoto image. curve such as bearing in, bearing out, and the optimum radius All the images went through the scaling and level process for the road curve. Two road curves have been designed which also referred to the orientations such as interior, with the radius of 50 and 60 meters for the first and second relative, and exterior orientation. It is demonstrated that the curve. After clicking the position of the Intersection Point UAV together with the digital camera are capable of acquiring and keying in the value of radius, bearing in and bearing out aerial photograph successfully for large scale mapping in a the sow ft are will calculate the other parameters values which short amount of time. This study shows that UAV is also are available for the designed road curve such as arc, chord, capable of producing road mapping at the selected study and tangent length. Figure 7 shows the finished road design area. All of the results are analysed using the Root Mean 6 International Journal of Optics Table 2: Analysis on disparity between conventional and UAV coordinates. CPs X(m) Y(m) Z(m) X (m) Y (m) Z (m) CP -0.371 -0.548 -0.693 0.138 0.300 0.480 CP 0.151 0.163 -0.156 0.023 0.027 0.024 CP 0.316 0.471 0.676 0.100 0.222 0.457 CP 0.125 0.262 0.290 0.016 0.069 0.084 CP 0.047 -0.062 -0.399 0.002 0.004 0.159 CP -0.030 -0.057 -0.500 0.001 0.003 0.250 CP 0.029 -0.099 -0.116 0.001 0.010 0.013 CP 0.048 -0.140 0.094 0.002 0.020 0.009 CP -0.069 -0.155 -0.349 0.005 0.024 0.122 CP -0.194 -0.190 -0.739 0.038 0.036 0.546 CP -0.013 -0.005 -0.240 0.000 0.000 0.058 CP -0.028 0.019 0.798 0.001 0.000 0.637 CP 0.049 0.071 0.553 0.002 0.005 0.306 CP 0.088 0.090 0.267 0.008 0.008 0.071 RMSE (m) 0.155 0.228 0.479 get more details for the topographical map to design a road. Different methods of camera calibration could be applied to optimise the quality of the UAV image processing. MyRTKnet data should be used as a base for better control of the coordinate GCP to apply on the UAV images for processing. Dieff rent flying heights should be applied for UAV to conduct better results for road design. Data Availability The data used to support the findings of this study are available from the corresponding author upon request. Figure 8: Visual Accuracy Assessments of Both Road Designs. Conflicts of Interest The authors declare that they have no conflicts of interest. Square Error (RMSE). RMSE can assess the accuracy of the Acknowledgments UAV images using the check points that have been measured on the ground for data validation analysis. The designed Faculty of Architecture, Planning, and Surveying Universiti road in this study is for proposing an alternative road to Teknologi MARA (UiTM), Research Management Institute increase the efficiency of road traffic flow. The Digital Terrain (RMi), and Ministry of Higher Education (MOHE) are Model (DTM) data from the UAV images processed is used greatly acknowledged for providing the fund BESTARI 600- to evaluate the geographic features where it must follow the IRMI/MyRA 5/3/BESTARI (001/2017) to enable this research design policy parameters in order to help control the design to be carried out. The authors would also like to thank of the road. The safety of the users must also be calculated by the people who were directly or indirectly involved in this selecting the super elevation of the proposed road. Therefore, research. the alternative road can be designed using the parameters for road design provided by public work department standards. References In the future, the accuracy of the orthophoto can be improved and enhanced by increasing the number of GCPs and CPs [1] Z. Jaal and J. Abdullah, “User’s Preferences of Highway Land- during data collection in the field using the GPS technique. scapes in Malaysia: A Review and Analysis of the Literature,” It can also minimise the RMSE in data processing. The use Procedia - Social and Behavioral Sciences,vol. 36, pp.265–272, of DTM (digital terrain model) for road design to check the accuracy of the road designed and to improve the validation [2] S. Mart´ınez, J. Ortiz, and M. L. Gil, “Geometric documentation of using UAV for road design construction. Different types of of historical pavements using automated digital photogram- UAV shouldbe usedto improve the efl xibility of the survey metry and high-density reconstruction algorithms,” Journal of works; for example, a fixed wing UAV can cover a large area to Archaeological Science,vol.53,pp.1–11, 2015. International Journal of Optics 7 [3] M. M. S. Albattah, “Optimum Highway Design and Site Loca- [20] K. N. Tahar and A. Ahmad, “Production of Orthophoto and tion Using Spatial Geoinformatics Engineering,” Journal of Volume Determination Using Low-Cost Digital Cameras,” Per- Remote Sensing & GIS,vol.5,no. 1,p.10,2016. tanika Journal of Science and Technology, vol.21,no. 2,pp.387– 396, 2011. [4] P.Kettunen,C.Koski, andJ.Oksanen,“Adesign ofcontourgen- [21] P. Xiaodong and L. Zongjian, “Unmanned airship low altitude eration for topographic maps with adaptive DEM smoothing,” International Journal of Cartography,vol.3,no.1,pp.19–30,2017. system for aerial photogrammetry,” Science of Surveying and Mapping, vol.34,no.4,pp.33–35, 2009. [5] F. van Coillie, K. Delaplace, D. Gabriels et al., “Monotempo- [22] X.Du,X.Jin,X.Zhang, J.Shen,andX.Hou,“Geometry features ral assessment of the population structure of Acacia tortilis measurement of traffic accident for reconstruction based on (Forssk.) Hayne ssp. raddiana (Savi) Brenan in Bou Hedma close-ra nge photogrammetry,” Advances in Engineering Soft- National Park, Tunisia: A terrestrial and remote sensing ware, vol.40, no.7,pp.497–505,2009. approach,” Journal of Arid Environments,vol. 129,pp. 80–92, 2016. [23] J. Chahl, “rTh ee biomimetic flight control sensors,” Interna- tional Journal of Intelligent Unmanned Systems,vol.2,no. 1,pp. [6] H. Eisenbeiss, UAV Photogrammetry, DISS. ETH NO. 18515,1- 27–39, 2014. 237, 2009. [24] H. Chao, Y. Cao, and Y. Chen, “Autopilots for small unmanned [7] Y. Fujii, E. Fodde, K. Watanabe, and K. Murakami, “Digital pho- aerial vehicles: A survey,” International Journal of Control, togrammetry for the documentation of structural damage in Automation, and Systems, vol.8,no.1,pp. 36–44, 2010. earthen archaeological sites: the case of Ajina Tepa, Tajikistan,” [25] F. Delgado, J. Carvalho, T. Coelho, and A. Dos Santos, “An Engineering Geology,vol.105, no.1-2,pp.124–133,2009. Optical Fiber Sensor and Its Application in UAVs for Current [8] Z. Lin, “UAV aiborne low altitude photogrammetry system,” Measurements,” Sensors, vol. 16, no. 11, p. 1800, 2016. Science of Surveying and Mapping, vol.36,no. 1, p.5,2011. [26] S. Jabari, F. Fathollahi, A. Roshan, and Y. Zhang, “Improving [9] M.A.Nu˜ ´nez, F. Buill, and M. Edo, “3D model of the Can UAV imaging quality by optical sensor fusion: an initial study,” Sadurn´ıcave,” Journal of Archaeological Science, vol.40, no.12, International Journal of Remote Sensing, vol.38, no.17,pp.4931– pp.4420–4428,2013. 4953, 2017. [10] H. Bendea, F. Chiabrando, F. Tonolo, and D. Marenchino, “Map- [27] A. B. Junaid, Y. Lee, and Y. Kim, “Design and implementation ping of archaeological areas using a low-cost UAV the Augusta of autonomous wireless charging station for rotary-wing UAVs,” Bagiennorum Test,” XXI International CIPA Symposium,pp. 1– Aerospace Science and Technology, vol.54,pp.253–266, 2016. 8, 2007. [28] H. Eisenbeiss, Photogrammetric Week ’11, F. Dieter, Ed., 2011, [11] I. Colomina and P. Molina, “Unmanned aerial systems for Wichmann/VDE Verlag, Belin & Offenbach. photogrammetry and remote sensing: a review,” ISPRS Journal [29] K. N. Tahar and A. Ahmad, “A simulation study on the capa- of Photogrammetry and Remote Sensing, vol.92, pp.79–97,2014. bilities of rotor wing unmanned aerial vehicle in aerial terrain [12] G. J. Grenzdor ¨ eff r, M. Guretzki, and I. Friedlander, “Pho- mapping,” International Journal of Physical Sciences,vol.7,no. togrammetric image acquisition and image analysis of oblique 8, 2012. imagery,” eTh Photogrammetric Record , vol.23,no.124,pp. 372– 386, 2008. [13] R. N. Jiang and D. V. Jauregui, “Development of a digital close- range photogrammetric bridge deflection measurement sys- tem,” Measurement,vol.43,no.10,pp.1431–1438, 2010. [14] T. Luhmann, “Close range photogrammetry for industrial applications,” ISPRS Journal of Photogrammetry and Remote Sensing, vol.65, no. 6,pp. 558–569,2010. [15] K. Konolige and M. Agrawal, “FrameSLAM: From bundle adjustment to real-time visual mapping,” IEEE Transactions on Robotics,vol.24,no.5,pp.1066–1077, 2008. [16] S. G. Kontogiannis and J. A. Ekaterinaris, “Design, performance evaluation and optimization of a UAV,” Aerospace Science and Technology, vol.29,no.1,pp.339–350,2013. [17] M. Rod ¨ er, H. Latifi, S. Hill et al., “Application of optical unmanned aerial vehicle-based imagery for the inventory of natural regeneration and standing deadwood in post-disturbed spruce forests,” International Journal of Remote Sensing,pp. 1– 22, 2018. [18] L. Sahar, S. Muthukumar, and S. P. French, “Using aerial imagery and gis in automated building footprint extraction and shape recognition for earthquake risk assessment of urban invento- ries,” IEEE Transactions on Geoscience and Remote Sensing,vol. 48, no. 9, pp. 3511–3520, 2010. [19] S. Pal Singh, K. Jain, and V. Mandla, “Virtual 3D Campus Mod- eling by Using Close Range Photogrammetry,” American Jour- nal of Civil Engineering and Architecture, vol.1,no.6,pp. 200– 205, 2013. 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