Automatic sensor orientation using horizontal and vertical line feature constraints.
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| Title: | Automatic sensor orientation using horizontal and vertical line feature constraints. |
|---|---|
| Authors: | Sun, Yanbiao1 (AUTHOR) Yanbiao.Sun@ucl.ac.uk, Robson, Stuart1 (AUTHOR) s.robson@ucl.ac.uk, Scott, Daniel1 (AUTHOR) daniel.scott.12@ucl.ac.uk, Boehm, Jan1 (AUTHOR) j.boehm@ucl.ac.uk, Wang, Qiang1 (AUTHOR) |
| Source: | ISPRS Journal of Photogrammetry & Remote Sensing. Apr2019, Vol. 150, p172-184. 13p. |
| Subjects: | Intelligent buildings, Detectors, Radarsat satellites |
| Abstract: | Abstract To improve the accuracy of sensor orientation using calibrated aerial images, this paper proposes an automatic sensor orientation method utilizing horizontal and vertical constraints on human-engineered structures, addressing the limitations faced with sub-optimal number of Ground Control Points (GCPs) within a scene. Related state-of-the-art methods rely on structured building edges, and necessitate manual identification of end points. Our method makes use of line-segments but eliminates the need for these matched end points, thus eliminating the need for inefficient manual intervention. To achieve this, a 3D line in object space is represented by the intersection of two planes going through two camera centers. The normal vector of each plane can be written as a function of a pair of azimuth and elevations angles. The normal vector of the 3D line can be expressed by the cross product of these two plane's normal vectors. Then, we create observation functions of horizontal and vertical line constraints based on the zero-vector cross-product and the dot-product of the normal vector of the 3D lines. The observation functions of the horizontal and vertical lines are then introduced into a hybrid Bundle Adjustment (BA) method as constraints, including observed image points as well as observed line segment projections. Finally, to assess the feasibility and effectiveness of the proposed method, simulated and real data are tested. The results demonstrate that, in cases with only 3 GCPs, the accuracy of the proposed method utilizing line features extracted automatically, is increased by 50%, compared to a BA using only point constraints. [ABSTRACT FROM AUTHOR] |
| Copyright of ISPRS Journal of Photogrammetry & Remote Sensing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 135399000 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Automatic sensor orientation using horizontal and vertical line feature constraints. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sun%2C+Yanbiao%22">Sun, Yanbiao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Yanbiao.Sun@ucl.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Robson%2C+Stuart%22">Robson, Stuart</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> s.robson@ucl.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Scott%2C+Daniel%22">Scott, Daniel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> daniel.scott.12@ucl.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Boehm%2C+Jan%22">Boehm, Jan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> j.boehm@ucl.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Qiang%22">Wang, Qiang</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22ISPRS+Journal+of+Photogrammetry+%26+Remote+Sensing%22">ISPRS Journal of Photogrammetry & Remote Sensing</searchLink>. Apr2019, Vol. 150, p172-184. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Intelligent+buildings%22">Intelligent buildings</searchLink><br /><searchLink fieldCode="DE" term="%22Detectors%22">Detectors</searchLink><br /><searchLink fieldCode="DE" term="%22Radarsat+satellites%22">Radarsat satellites</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract To improve the accuracy of sensor orientation using calibrated aerial images, this paper proposes an automatic sensor orientation method utilizing horizontal and vertical constraints on human-engineered structures, addressing the limitations faced with sub-optimal number of Ground Control Points (GCPs) within a scene. Related state-of-the-art methods rely on structured building edges, and necessitate manual identification of end points. Our method makes use of line-segments but eliminates the need for these matched end points, thus eliminating the need for inefficient manual intervention. To achieve this, a 3D line in object space is represented by the intersection of two planes going through two camera centers. The normal vector of each plane can be written as a function of a pair of azimuth and elevations angles. The normal vector of the 3D line can be expressed by the cross product of these two plane's normal vectors. Then, we create observation functions of horizontal and vertical line constraints based on the zero-vector cross-product and the dot-product of the normal vector of the 3D lines. The observation functions of the horizontal and vertical lines are then introduced into a hybrid Bundle Adjustment (BA) method as constraints, including observed image points as well as observed line segment projections. Finally, to assess the feasibility and effectiveness of the proposed method, simulated and real data are tested. The results demonstrate that, in cases with only 3 GCPs, the accuracy of the proposed method utilizing line features extracted automatically, is increased by 50%, compared to a BA using only point constraints. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of ISPRS Journal of Photogrammetry & Remote Sensing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.isprsjprs.2019.02.011 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 172 Subjects: – SubjectFull: Intelligent buildings Type: general – SubjectFull: Detectors Type: general – SubjectFull: Radarsat satellites Type: general Titles: – TitleFull: Automatic sensor orientation using horizontal and vertical line feature constraints. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sun, Yanbiao – PersonEntity: Name: NameFull: Robson, Stuart – PersonEntity: Name: NameFull: Scott, Daniel – PersonEntity: Name: NameFull: Boehm, Jan – PersonEntity: Name: NameFull: Wang, Qiang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 09242716 Numbering: – Type: volume Value: 150 Titles: – TitleFull: ISPRS Journal of Photogrammetry & Remote Sensing Type: main |
| ResultId | 1 |
