Evaluating the performance of sUAS photogrammetry with PPK positioning for infrastructure mapping
| dc.contributor.author | McMahon, Conor | |
| dc.contributor.author | Mora, Omar E. | |
| dc.contributor.author | Starek, Michael J. | |
| dc.creator.orcid | https://orcid.org/0000-0003-4162-6168 | en_US |
| dc.creator.orcid | https://orcid.org/0000-0002-5884-9205 | en_US |
| dc.creator.orcid | https://orcid.org/0000-0002-7996-0594 | en_US |
| dc.creator.orcid | https://orcid.org/0000-0003-4162-6168 | |
| dc.creator.orcid | https://orcid.org/0000-0002-5884-9205 | |
| dc.creator.orcid | https://orcid.org/0000-0002-7996-0594 | |
| dc.creator.orcid | https://orcid.org/0000-0003-4162-6168 | |
| dc.creator.orcid | https://orcid.org/0000-0002-5884-9205 | |
| dc.creator.orcid | https://orcid.org/0000-0002-7996-0594https://orcid.org/0000-0003-4162-6168 | |
| dc.creator.orcid | https://orcid.org/0000-0002-5884-9205 | |
| dc.creator.orcid | https://orcid.org/0000-0002-7996-0594 | |
| dc.date.accessioned | 2021-10-20T19:53:11Z | |
| dc.date.available | 2021-10-20T19:53:11Z | |
| dc.date.issued | 2021-06-01 | |
| dc.description.abstract | Traditional acquisition methods for generating digital surface models (DSMs) of infrastructure are either low resolution and slow (total station-based methods) or expensive (LiDAR). By contrast, photogrammetric methods have recently received attention due to their ability to generate dense 3D models quickly for low cost. However, existing frameworks often utilize many manually measured control points, require a permanent RTK/PPK reference station, or yield a reconstruction accuracy too poor to be useful in many applications. In addition, the causes of inaccuracy in photogrammetric imagery are complex and sometimes not well understood. In this study, a small unmanned aerial system (sUAS) was used to rapidly image a relatively even, 1 ha ground surface. Model accuracy was investigated to determine the importance of ground control point (GCP) count and differential GNSS base station type. Results generally showed the best performance for tests using five or more GCPs or when a Continuously Operating Reference Station (CORS) was used, with vertical root mean square errors of 0.026 and 0.027 m in these cases. However, accuracy outputs generally met comparable published results in the literature, demonstrating the viability of analyses relying solely on a temporary local base with a one-hour dwell time and no GCPs. | en_US |
| dc.description.abstract | Traditional acquisition methods for generating digital surface models (DSMs) of infrastructure are either low resolution and slow (total station-based methods) or expensive (LiDAR). By contrast, photogrammetric methods have recently received attention due to their ability to generate dense 3D models quickly for low cost. However, existing frameworks often utilize many manually measured control points, require a permanent RTK/PPK reference station, or yield a reconstruction accuracy too poor to be useful in many applications. In addition, the causes of inaccuracy in photogrammetric imagery are complex and sometimes not well understood. In this study, a small unmanned aerial system (sUAS) was used to rapidly image a relatively even, 1 ha ground surface. Model accuracy was investigated to determine the importance of ground control point (GCP) count and differential GNSS base station type. Results generally showed the best performance for tests using five or more GCPs or when a Continuously Operating Reference Station (CORS) was used, with vertical root mean square errors of 0.026 and 0.027 m in these cases. However, accuracy outputs generally met comparable published results in the literature, demonstrating the viability of analyses relying solely on a temporary local base with a one-hour dwell time and no GCPs. | |
| dc.identifier.citation | McMahon, C.; Mora, O.E.; Starek, M.J. Evaluating the Performance of sUAS Photogrammetry with PPK Positioning for Infrastructure Mapping. Drones 2021, 5, 50. https://doi.org/10.3390/ drones5020050 | en_US |
| dc.identifier.citation | McMahon, C.; Mora, O.E.; Starek, M.J. Evaluating the Performance of sUAS Photogrammetry with PPK Positioning for Infrastructure Mapping. Drones 2021, 5, 50. https://doi.org/10.3390/ drones5020050 | |
| dc.identifier.doi | https://doi.org/10.3390/drones5020050 | |
| dc.identifier.uri | https://hdl.handle.net/1969.6/89839 | |
| dc.language.iso | en_US | en_US |
| dc.language.iso | en_US | |
| dc.publisher | MDPI | en_US |
| dc.publisher | MDPI | |
| dc.rights | Attribution 4.0 International | * |
| dc.rights | Attribution 4.0 International | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | suas | en_US |
| dc.subject | photogrammetry | en_US |
| dc.subject | mapping | en_US |
| dc.subject | dsm | en_US |
| dc.subject | ppk | en_US |
| dc.subject | gcp | en_US |
| dc.subject | suas | |
| dc.subject | photogrammetry | |
| dc.subject | mapping | |
| dc.subject | dsm | |
| dc.subject | ppk | |
| dc.subject | gcp | |
| dc.title | Evaluating the performance of sUAS photogrammetry with PPK positioning for infrastructure mapping | en_US |
| dc.title | Evaluating the performance of sUAS photogrammetry with PPK positioning for infrastructure mapping | |
| dc.type | Article | en_US |
| dc.type | Article |