A structure-from-motion pipeline for topographic reconstructions using unmanned aerial vehicles and open source software
In recent years, the generation of accurate topographic reconstructions has found applications ranging from geomorphic sciences to remote sensing and urban planning, among others. The production of high resolution, high-quality digital elevation models (DEMs) requires a significant investment in per...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8910
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8910
- Palabra clave:
- Geomatics
Open source software
Structure from motion
Antennas
Cameras
Cost effectiveness
Object recognition
Open systems
Optical radar
Photogrammetry
Pipelines
Remote sensing
Repair
Rock mechanics
Surveying
Tracking radar
Unmanned aerial vehicles (UAV)
Verification
Co-ordinate system
Computer vision techniques
Digital elevation model
Experimental verification
Geomatics
Ground control points
Structure from motion
Topographic reconstruction
Open source software
- Rights
- restrictedAccess
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- http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.title.none.fl_str_mv |
A structure-from-motion pipeline for topographic reconstructions using unmanned aerial vehicles and open source software |
title |
A structure-from-motion pipeline for topographic reconstructions using unmanned aerial vehicles and open source software |
spellingShingle |
A structure-from-motion pipeline for topographic reconstructions using unmanned aerial vehicles and open source software Geomatics Open source software Structure from motion Antennas Cameras Cost effectiveness Object recognition Open systems Optical radar Photogrammetry Pipelines Remote sensing Repair Rock mechanics Surveying Tracking radar Unmanned aerial vehicles (UAV) Verification Co-ordinate system Computer vision techniques Digital elevation model Experimental verification Geomatics Ground control points Structure from motion Topographic reconstruction Open source software |
title_short |
A structure-from-motion pipeline for topographic reconstructions using unmanned aerial vehicles and open source software |
title_full |
A structure-from-motion pipeline for topographic reconstructions using unmanned aerial vehicles and open source software |
title_fullStr |
A structure-from-motion pipeline for topographic reconstructions using unmanned aerial vehicles and open source software |
title_full_unstemmed |
A structure-from-motion pipeline for topographic reconstructions using unmanned aerial vehicles and open source software |
title_sort |
A structure-from-motion pipeline for topographic reconstructions using unmanned aerial vehicles and open source software |
dc.contributor.editor.none.fl_str_mv |
Serrano C. J.E. Martínez-Santos, Juan Carlos |
dc.subject.keywords.none.fl_str_mv |
Geomatics Open source software Structure from motion Antennas Cameras Cost effectiveness Object recognition Open systems Optical radar Photogrammetry Pipelines Remote sensing Repair Rock mechanics Surveying Tracking radar Unmanned aerial vehicles (UAV) Verification Co-ordinate system Computer vision techniques Digital elevation model Experimental verification Geomatics Ground control points Structure from motion Topographic reconstruction Open source software |
topic |
Geomatics Open source software Structure from motion Antennas Cameras Cost effectiveness Object recognition Open systems Optical radar Photogrammetry Pipelines Remote sensing Repair Rock mechanics Surveying Tracking radar Unmanned aerial vehicles (UAV) Verification Co-ordinate system Computer vision techniques Digital elevation model Experimental verification Geomatics Ground control points Structure from motion Topographic reconstruction Open source software |
description |
In recent years, the generation of accurate topographic reconstructions has found applications ranging from geomorphic sciences to remote sensing and urban planning, among others. The production of high resolution, high-quality digital elevation models (DEMs) requires a significant investment in personnel time, hardware, and software. Photogrammetry offers clear advantages over other methods of collecting geomatic information. Airborne cameras can cover large areas more quickly than ground survey techniques, and the generated Photogrammetry-based DEMs often have higher resolution than models produced with other remote sensing methods such as LIDAR (Laser Imaging Detection and Ranging) or RADAR (radar detection and ranging). In this work, we introduce a Structure from Motion (SfM) pipeline using Unmanned Aerial Vehicles (UAVs) for generating DEMs for performing topographic reconstructions and assessing the microtopography of a terrain. SfM is a computer vision technique that consists in estimating the 3D coordinates of many points in a scene using two or more 2D images acquired from different positions. By identifying common points in the images both the camera position (motion) and the 3D locations of the points (structure) are obtained. The output from an SfM stage is a sparse point cloud in a local XYZ coordinate system. We edit the obtained point in MeshLab to remove unwanted points, such as those from vehicles, roofs, and vegetation. We scale the XYZ point clouds using Ground Control Points (GCP) and GPS information. This process enables georeferenced metric measurements. For the experimental verification, we reconstructed a terrain suitable for subsequent analysis using GIS software. Encouraging results show that our approach is highly cost-effective, providing a means for generating high-quality, low-cost DEMs. © Springer Nature Switzerland AG 2018. |
publishDate |
2018 |
dc.date.issued.none.fl_str_mv |
2018 |
dc.date.accessioned.none.fl_str_mv |
2020-03-26T16:32:35Z |
dc.date.available.none.fl_str_mv |
2020-03-26T16:32:35Z |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_c94f |
dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
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info:eu-repo/semantics/publishedVersion |
dc.type.spa.none.fl_str_mv |
Conferencia |
status_str |
publishedVersion |
dc.identifier.citation.none.fl_str_mv |
Communications in Computer and Information Science; Vol. 885, pp. 213-225 |
dc.identifier.isbn.none.fl_str_mv |
9783319989976 |
dc.identifier.issn.none.fl_str_mv |
18650929 |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/8910 |
dc.identifier.doi.none.fl_str_mv |
10.1007/978-3-319-98998-3_17 |
dc.identifier.instname.none.fl_str_mv |
Universidad Tecnológica de Bolívar |
dc.identifier.reponame.none.fl_str_mv |
Repositorio UTB |
dc.identifier.orcid.none.fl_str_mv |
57204065355 24329839300 56682678200 57200615582 7004348301 36142156300 |
identifier_str_mv |
Communications in Computer and Information Science; Vol. 885, pp. 213-225 9783319989976 18650929 10.1007/978-3-319-98998-3_17 Universidad Tecnológica de Bolívar Repositorio UTB 57204065355 24329839300 56682678200 57200615582 7004348301 36142156300 |
url |
https://hdl.handle.net/20.500.12585/8910 |
dc.language.iso.none.fl_str_mv |
eng |
language |
eng |
dc.relation.conferencedate.none.fl_str_mv |
26 September 2018 through 28 September 2018 |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_16ec |
dc.rights.uri.none.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/restrictedAccess |
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Atribución-NoComercial 4.0 Internacional |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial 4.0 Internacional http://purl.org/coar/access_right/c_16ec |
eu_rights_str_mv |
restrictedAccess |
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Recurso electrónico |
dc.format.mimetype.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer Verlag |
publisher.none.fl_str_mv |
Springer Verlag |
dc.source.none.fl_str_mv |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054350508&doi=10.1007%2f978-3-319-98998-3_17&partnerID=40&md5=6fa9e5e6a5410c02c669bb5dc5e2af6f |
institution |
Universidad Tecnológica de Bolívar |
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13th Colombian Conference on Computing, CCC 2018 |
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spelling |
Serrano C. J.E.Martínez-Santos, Juan CarlosMeza J.Marrugo A.G.Sierra E.Guerrero M.Meneses J.Romero L.A.2020-03-26T16:32:35Z2020-03-26T16:32:35Z2018Communications in Computer and Information Science; Vol. 885, pp. 213-225978331998997618650929https://hdl.handle.net/20.500.12585/891010.1007/978-3-319-98998-3_17Universidad Tecnológica de BolívarRepositorio UTB57204065355243298393005668267820057200615582700434830136142156300In recent years, the generation of accurate topographic reconstructions has found applications ranging from geomorphic sciences to remote sensing and urban planning, among others. The production of high resolution, high-quality digital elevation models (DEMs) requires a significant investment in personnel time, hardware, and software. Photogrammetry offers clear advantages over other methods of collecting geomatic information. Airborne cameras can cover large areas more quickly than ground survey techniques, and the generated Photogrammetry-based DEMs often have higher resolution than models produced with other remote sensing methods such as LIDAR (Laser Imaging Detection and Ranging) or RADAR (radar detection and ranging). In this work, we introduce a Structure from Motion (SfM) pipeline using Unmanned Aerial Vehicles (UAVs) for generating DEMs for performing topographic reconstructions and assessing the microtopography of a terrain. SfM is a computer vision technique that consists in estimating the 3D coordinates of many points in a scene using two or more 2D images acquired from different positions. By identifying common points in the images both the camera position (motion) and the 3D locations of the points (structure) are obtained. The output from an SfM stage is a sparse point cloud in a local XYZ coordinate system. We edit the obtained point in MeshLab to remove unwanted points, such as those from vehicles, roofs, and vegetation. We scale the XYZ point clouds using Ground Control Points (GCP) and GPS information. This process enables georeferenced metric measurements. For the experimental verification, we reconstructed a terrain suitable for subsequent analysis using GIS software. Encouraging results show that our approach is highly cost-effective, providing a means for generating high-quality, low-cost DEMs. © Springer Nature Switzerland AG 2018.Acknowledgement. This work has been partly funded by Universidad Tecnológica de Bolívar project (FI2006T2001). E. Sierra thanks Universidad Tecnológica de Bolívar for a Masters degree scholarship.Recurso electrónicoapplication/pdfengSpringer Verlaghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85054350508&doi=10.1007%2f978-3-319-98998-3_17&partnerID=40&md5=6fa9e5e6a5410c02c669bb5dc5e2af6f13th Colombian Conference on Computing, CCC 2018A structure-from-motion pipeline for topographic reconstructions using unmanned aerial vehicles and open source softwareinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fGeomaticsOpen source softwareStructure from motionAntennasCamerasCost effectivenessObject recognitionOpen systemsOptical radarPhotogrammetryPipelinesRemote sensingRepairRock mechanicsSurveyingTracking radarUnmanned aerial vehicles (UAV)VerificationCo-ordinate systemComputer vision techniquesDigital elevation modelExperimental verificationGeomaticsGround control pointsStructure from motionTopographic reconstructionOpen source software26 September 2018 through 28 September 2018Nelson, A., Reuter, H., Gessler, P., DEM production methods and sources (2009) Dev. Soil Sci., 33, pp. 65-85Carbonneau, P.E., Dietrich, J.T., Cost-effective non-metric photogrammetry from consumer-grade sUAS: Implications for direct georeferencing of structure from motion photogrammetry (2016) Earth Surf. Process. Land., 42, pp. 473-486Nex, F., Remondino, F., UAV for 3D mapping applications: A review (2014) Appl. Geomat., 6 (1), pp. 1-15James, M., Robson, S., Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application (2012) J. Geophys. Res. Earth Surf., 117 (F3)Fonstad, M.A., Dietrich, J.T., Courville, B.C., Jensen, J.L., Carbonneau, P.E., Topographic structure from motion: A new development in photogrammetric measurement (2013) Earth Surf. Process. Land., 38, pp. 421-430Goesele, M., Curless, B., Seitz, S.M., Multi-view stereo revisited (2006) 2009 IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2009, pp. 2402-2409. , IEEEAgisoft Photoscan Professional, , http://www.agisoft.com/downloads/installer/ https://github.com/mapillary/OpenSfMOpendronemap, , https://github.com/OpenDroneMap/OpenDroneMapBradski, G., Kaehler, A., OpenCV. Dr. Dobb’s (2000) J. Softw. Tools., 3 https://www.altizure.comCignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., Ranzuglia, G., MeshLab: An open-source mesh processing tool (2008) Eurographics Italian Chapter Conference, 2008, pp. 129-136Duane, C.B., Close-range camera calibration (1971) Photogram. Eng, 37 (8), pp. 855-866 https://maps.google.comTuytelaars, T., Mikolajczyk, K., Local invariant feature detectors: A survey (2008) Found. Trends® Comput. Graph. Vis., 3 (3), pp. 177-280Bolick, L., Harguess, J., A study of the effects of degraded imagery on tactical 3D model generation using structure-from-motion (2016) Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications XIII, 9828. , International Society for Optics and PhotonicsGrauman, K., Leibe, B., Visual object recognition (2011) Synthesis Lectures on Artificial Intelligence and Machine Learning, 5 (2), pp. 1-181Lindeberg, T., Feature detection with automatic scale selection (1998) Int. J. Comput. Vis., 30 (2), pp. 79-116Muja, M., Lowe, D.G., Fast approximate nearest neighbors with automatic algorithm configuration (2009) VISAPP (1), 2 (331-340), p. 2Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W., Bundle adjustment—a modern synthesis (2000) IWVA 1999. LNCS, 1883, pp. 298-372. , https://doi.org/10.1007/3-540-44480-721, Triggs, B., Zisserman, A., Szeliski, R. (eds.), Springer, HeidelbergFurukawa, Y., Ponce, J., Accurate, dense, and robust multiview stereopsis (2010) IEEE Trans. Pattern Anal. Mach. Intell., 32 (8), pp. 1362-1376Adorjan, M., (2016) Opensfm Ein Kollaboratives Structure-From-Motion System, , betreuer/in (nen): M. wimmer, m. birsakinstitut für computergraphik und algorithmen. abschlussprüfung: 02.05.2016http://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/8910/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/8910oai:repositorio.utb.edu.co:20.500.12585/89102023-05-26 16:29:46.39Repositorio Institucional UTBrepositorioutb@utb.edu.co |