Incorporation of unreliable information into photogrammetric reconstruction for recovery of scale using non-parametric belief propagation
The creation of large photogrammetric models often encounter several difficulties in regards to geometric accuracy, scale and geolocation, especially when not using control points. Geometric accuracy can be a problem when encountering repetitive features, scale and geolocation can be challenging in...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/12106 |
| _version_ | 1848747985852170240 |
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| author | Hollick, J. Helmholz, Petra Belton, D. |
| author_facet | Hollick, J. Helmholz, Petra Belton, D. |
| author_sort | Hollick, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The creation of large photogrammetric models often encounter several difficulties in regards to geometric accuracy, scale and geolocation, especially when not using control points. Geometric accuracy can be a problem when encountering repetitive features, scale and geolocation can be challenging in GNSS denied or difficult to reach environments. Despite these challenges scale and location are often highly desirable even if only approximate, especially when the error bounds are known. Using non-parametric belief propagation we propose a method of fusing different sensor types to allow robust creation of scaled models without control points. Using this technique we scale models using only the sensor data sometimes to within 4% of their actual size even in the presence of poor GNSS coverage. |
| first_indexed | 2025-11-14T06:57:51Z |
| format | Conference Paper |
| id | curtin-20.500.11937-12106 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:57:51Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-121062017-09-13T14:59:18Z Incorporation of unreliable information into photogrammetric reconstruction for recovery of scale using non-parametric belief propagation Hollick, J. Helmholz, Petra Belton, D. The creation of large photogrammetric models often encounter several difficulties in regards to geometric accuracy, scale and geolocation, especially when not using control points. Geometric accuracy can be a problem when encountering repetitive features, scale and geolocation can be challenging in GNSS denied or difficult to reach environments. Despite these challenges scale and location are often highly desirable even if only approximate, especially when the error bounds are known. Using non-parametric belief propagation we propose a method of fusing different sensor types to allow robust creation of scaled models without control points. Using this technique we scale models using only the sensor data sometimes to within 4% of their actual size even in the presence of poor GNSS coverage. 2016 Conference Paper http://hdl.handle.net/20.500.11937/12106 10.5194/isprsarchives-XLI-B5-653-2016 unknown |
| spellingShingle | Hollick, J. Helmholz, Petra Belton, D. Incorporation of unreliable information into photogrammetric reconstruction for recovery of scale using non-parametric belief propagation |
| title | Incorporation of unreliable information into photogrammetric reconstruction for recovery of scale using non-parametric belief propagation |
| title_full | Incorporation of unreliable information into photogrammetric reconstruction for recovery of scale using non-parametric belief propagation |
| title_fullStr | Incorporation of unreliable information into photogrammetric reconstruction for recovery of scale using non-parametric belief propagation |
| title_full_unstemmed | Incorporation of unreliable information into photogrammetric reconstruction for recovery of scale using non-parametric belief propagation |
| title_short | Incorporation of unreliable information into photogrammetric reconstruction for recovery of scale using non-parametric belief propagation |
| title_sort | incorporation of unreliable information into photogrammetric reconstruction for recovery of scale using non-parametric belief propagation |
| url | http://hdl.handle.net/20.500.11937/12106 |