Abstract
In this paper, we address the problem of line triangulation, which is to find the position of a line in space given its three projections taken with cameras with known camera matrices. Because of measurement error in line extraction, the problem becomes difficult so that it is necessary to estimate a 3D line to optimally fit measured lines. In this work, the normal errors of measured line are presented to describe the measurement error and based on their statistical property a new geometric-distance optimality criterion is constructed. Furthermore, a simple iterative algorithm is proposed to obtain suboptimal solution of the optimality criterion, which ensures that the solution satisfies the trifocal tensor constraint. Experiments show that our iterative algorithm can achieve the estimation accuracy comparable with the Gold Standard algorithm, but its computational load is substantially reduced.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
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Zhang, Q., Wu, Y., Liu, M., Jiao, L. (2013). An Efficient Normal-Error Iterative Algorithm for Line Triangulation. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_27
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DOI: https://doi.org/10.1007/978-3-319-02895-8_27
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