Relative pose estimation using image feature triplets

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Chuang, T.Y.; Rottensteiner, F.; Heipke, C.: Relative pose estimation using image feature triplets. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 40 (2015), Nr. 3W2, S. 39-45. DOI: https://doi.org/10.5194/isprsarchives-XL-3-W2-39-2015

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/875

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Sum total of downloads: 173




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Abstract: 
A fully automated reconstruction of the trajectory of image sequences using point correspondences is turning into a routine practice. However, there are cases in which point features are hardly detectable, cannot be localized in a stable distribution, and consequently lead to an insufficient pose estimation. This paper presents a triplet-wise scheme for calibrated relative pose estimation from image point and line triplets, and investigates the effectiveness of the feature integration upon the relative pose estimation. To this end, we employ an existing point matching technique and propose a method for line triplet matching in which the relative poses are resolved during the matching procedure. The line matching method aims at establishing hypotheses about potential minimal line matches that can be used for determining the parameters of relative orientation (pose estimation) of two images with respect to the reference one; then, quantifying the agreement using the estimated orientation parameters. Rather than randomly choosing the line candidates in the matching process, we generate an associated lookup table to guide the selection of potential line matches. In addition, we integrate the homologous point and line triplets into a common adjustment procedure. In order to be able to also work with image sequences the adjustment is formulated in an incremental manner. The proposed scheme is evaluated with both synthetic and real datasets, demonstrating its satisfactory performance and revealing the effectiveness of image feature integration.
License of this version: CC BY 3.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2015
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 123 71.10%
2 image of flag of United States United States 16 9.25%
3 image of flag of No geo information available No geo information available 4 2.31%
4 image of flag of Taiwan Taiwan 4 2.31%
5 image of flag of China China 4 2.31%
6 image of flag of Russian Federation Russian Federation 3 1.73%
7 image of flag of Nepal Nepal 2 1.16%
8 image of flag of Hong Kong Hong Kong 2 1.16%
9 image of flag of United Kingdom United Kingdom 2 1.16%
10 image of flag of Estonia Estonia 2 1.16%
    other countries 11 6.36%

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