A Multi-Resolution Approach to Point Cloud Registration without Control Points
Terrestrial photographic imagery combined with structure-from-motion (SfM) provides a relatively easy-to-implement method for monitoring environmental systems, even in remote and rough terrain. However, the collection of in-situ positioning data and the identification of control points required for...
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ftmdpi:oai:mdpi.com:/2072-4292/15/4/1161/ 2023-08-20T04:06:44+02:00 A Multi-Resolution Approach to Point Cloud Registration without Control Points Eleanor A. Bash Lakin Wecker Mir Mustafizur Rahman Christine F. Dow Greg McDermid Faramarz F. Samavati Ken Whitehead Brian J. Moorman Dorota Medrzycka Luke Copland agris 2023-02-20 application/pdf https://doi.org/10.3390/rs15041161 EN eng Multidisciplinary Digital Publishing Institute Environmental Remote Sensing https://dx.doi.org/10.3390/rs15041161 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 15; Issue 4; Pages: 1161 photogrammetry structure-from-motion Discrete Global Grid System DGGS change detection point cloud registration Text 2023 ftmdpi https://doi.org/10.3390/rs15041161 2023-08-01T08:54:13Z Terrestrial photographic imagery combined with structure-from-motion (SfM) provides a relatively easy-to-implement method for monitoring environmental systems, even in remote and rough terrain. However, the collection of in-situ positioning data and the identification of control points required for georeferencing in SfM processing is the primary roadblock to using SfM in difficult-to-access locations; it is also the primary bottleneck for using SfM in a time series. We describe a novel, computationally efficient, and semi-automated approach for georeferencing unreferenced point clouds (UPC) derived from terrestrial overlapping photos to a reference dataset (e.g., DEM or aerial point cloud; hereafter RPC) in order to address this problem. The approach utilizes a Discrete Global Grid System (DGGS), which allows us to capitalize on easily collected rough information about camera deployment to coarsely register the UPC using the RPC. The DGGS also provides a hierarchical set of grids which supports a hierarchical modified iterative closest point algorithm with natural correspondence between the UPC and RPC. The approach requires minimal interaction in a user-friendly interface, while allowing for user adjustment of parameters and inspection of results. We illustrate the approach with two case studies: a close-range (<1 km) vertical glacier calving front reconstructed from two cameras at Fountain Glacier, Nunavut and a long-range (>3 km) scene of relatively flat glacier ice reconstructed from four cameras overlooking Nàłùdäy (Lowell Glacier), Yukon, Canada. We assessed the accuracy of the georeferencing by comparing the UPC to the RPC, as well as surveyed control points; the consistency of the registration was assessed using the difference between successive registered surfaces in the time series. The accuracy of the registration is roughly equal to the ground sampling distance and is consistent across time steps. These results demonstrate the promise of the approach for easy-to-implement georeferencing of ... Text glacier* Nunavut Yukon MDPI Open Access Publishing Nunavut Yukon Canada Fountain Glacier ENVELOPE(161.633,161.633,-77.683,-77.683) Lowell Glacier ENVELOPE(-138.254,-138.254,60.299,60.299) Remote Sensing 15 4 1161 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
photogrammetry structure-from-motion Discrete Global Grid System DGGS change detection point cloud registration |
spellingShingle |
photogrammetry structure-from-motion Discrete Global Grid System DGGS change detection point cloud registration Eleanor A. Bash Lakin Wecker Mir Mustafizur Rahman Christine F. Dow Greg McDermid Faramarz F. Samavati Ken Whitehead Brian J. Moorman Dorota Medrzycka Luke Copland A Multi-Resolution Approach to Point Cloud Registration without Control Points |
topic_facet |
photogrammetry structure-from-motion Discrete Global Grid System DGGS change detection point cloud registration |
description |
Terrestrial photographic imagery combined with structure-from-motion (SfM) provides a relatively easy-to-implement method for monitoring environmental systems, even in remote and rough terrain. However, the collection of in-situ positioning data and the identification of control points required for georeferencing in SfM processing is the primary roadblock to using SfM in difficult-to-access locations; it is also the primary bottleneck for using SfM in a time series. We describe a novel, computationally efficient, and semi-automated approach for georeferencing unreferenced point clouds (UPC) derived from terrestrial overlapping photos to a reference dataset (e.g., DEM or aerial point cloud; hereafter RPC) in order to address this problem. The approach utilizes a Discrete Global Grid System (DGGS), which allows us to capitalize on easily collected rough information about camera deployment to coarsely register the UPC using the RPC. The DGGS also provides a hierarchical set of grids which supports a hierarchical modified iterative closest point algorithm with natural correspondence between the UPC and RPC. The approach requires minimal interaction in a user-friendly interface, while allowing for user adjustment of parameters and inspection of results. We illustrate the approach with two case studies: a close-range (<1 km) vertical glacier calving front reconstructed from two cameras at Fountain Glacier, Nunavut and a long-range (>3 km) scene of relatively flat glacier ice reconstructed from four cameras overlooking Nàłùdäy (Lowell Glacier), Yukon, Canada. We assessed the accuracy of the georeferencing by comparing the UPC to the RPC, as well as surveyed control points; the consistency of the registration was assessed using the difference between successive registered surfaces in the time series. The accuracy of the registration is roughly equal to the ground sampling distance and is consistent across time steps. These results demonstrate the promise of the approach for easy-to-implement georeferencing of ... |
format |
Text |
author |
Eleanor A. Bash Lakin Wecker Mir Mustafizur Rahman Christine F. Dow Greg McDermid Faramarz F. Samavati Ken Whitehead Brian J. Moorman Dorota Medrzycka Luke Copland |
author_facet |
Eleanor A. Bash Lakin Wecker Mir Mustafizur Rahman Christine F. Dow Greg McDermid Faramarz F. Samavati Ken Whitehead Brian J. Moorman Dorota Medrzycka Luke Copland |
author_sort |
Eleanor A. Bash |
title |
A Multi-Resolution Approach to Point Cloud Registration without Control Points |
title_short |
A Multi-Resolution Approach to Point Cloud Registration without Control Points |
title_full |
A Multi-Resolution Approach to Point Cloud Registration without Control Points |
title_fullStr |
A Multi-Resolution Approach to Point Cloud Registration without Control Points |
title_full_unstemmed |
A Multi-Resolution Approach to Point Cloud Registration without Control Points |
title_sort |
multi-resolution approach to point cloud registration without control points |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/rs15041161 |
op_coverage |
agris |
long_lat |
ENVELOPE(161.633,161.633,-77.683,-77.683) ENVELOPE(-138.254,-138.254,60.299,60.299) |
geographic |
Nunavut Yukon Canada Fountain Glacier Lowell Glacier |
geographic_facet |
Nunavut Yukon Canada Fountain Glacier Lowell Glacier |
genre |
glacier* Nunavut Yukon |
genre_facet |
glacier* Nunavut Yukon |
op_source |
Remote Sensing; Volume 15; Issue 4; Pages: 1161 |
op_relation |
Environmental Remote Sensing https://dx.doi.org/10.3390/rs15041161 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs15041161 |
container_title |
Remote Sensing |
container_volume |
15 |
container_issue |
4 |
container_start_page |
1161 |
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