ASP<I>n</I>P: An Accurate and Scalable Solution to the Perspective-<I>n</I>-Point Problem

Yinqiang ZHENG; Shigeki SUGIMOTO; Masatoshi OKUTOMI

doi:10.1587/transinf.E96.D.1525

ASPnP: An Accurate and Scalable Solution to the Perspective-n-Point Problem

Yinqiang ZHENG, Shigeki SUGIMOTO, Masatoshi OKUTOMI

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We propose an accurate and scalable solution to the perspective-n-point problem, referred to as ASPnP. Our main idea is to estimate the orientation and position parameters by directly minimizing a properly defined algebraic error. By using a novel quaternion representation of the rotation, our solution is immune to any parametrization degeneracy. To obtain the global optimum, we use the Grobner basis technique to solve the polynomial system derived from the first-order optimality condition. The main advantages of our proposed solution lie in accuracy and scalability. Extensive experiment results, with both synthetic and real data, demonstrate that our proposed solution has better accuracy than the state-of-the-art noniterative solutions. More importantly, by exploiting vectorization operations, the computational cost of our ASPnP solution is almost constant, independent of the number of point correspondences n in the wide range from 4 to 1000. In our experiment settings, the ASPnP solution takes about 4 milliseconds, thus best suited for real-time applications with a drastically varying number of 3D-to-2D point correspondences.

Publication: IEICE TRANSACTIONS on Information Vol.E96-D No.7 pp.1525-1535

Publication Date: 2013/07/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E96.D.1525

Type of Manuscript: PAPER

Category: Image Recognition, Computer Vision

Authors

Yinqiang ZHENG
  Tokyo Institute of Technology
Shigeki SUGIMOTO
  Tokyo Institute of Technology
Masatoshi OKUTOMI
  Tokyo Institute of Technology

Keyword

pose estimation, perspective-n-point, Grobner basis, global optimum

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Yinqiang ZHENG, Shigeki SUGIMOTO, Masatoshi OKUTOMI, "ASPnP: An Accurate and Scalable Solution to the Perspective-n-Point Problem" in IEICE TRANSACTIONS on Information, vol. E96-D, no. 7, pp. 1525-1535, July 2013, doi: 10.1587/transinf.E96.D.1525.
Abstract: We propose an accurate and scalable solution to the perspective-n-point problem, referred to as ASPnP. Our main idea is to estimate the orientation and position parameters by directly minimizing a properly defined algebraic error. By using a novel quaternion representation of the rotation, our solution is immune to any parametrization degeneracy. To obtain the global optimum, we use the Grobner basis technique to solve the polynomial system derived from the first-order optimality condition. The main advantages of our proposed solution lie in accuracy and scalability. Extensive experiment results, with both synthetic and real data, demonstrate that our proposed solution has better accuracy than the state-of-the-art noniterative solutions. More importantly, by exploiting vectorization operations, the computational cost of our ASPnP solution is almost constant, independent of the number of point correspondences n in the wide range from 4 to 1000. In our experiment settings, the ASPnP solution takes about 4 milliseconds, thus best suited for real-time applications with a drastically varying number of 3D-to-2D point correspondences.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.1525/_p

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@ARTICLE{e96-d_7_1525,
author={Yinqiang ZHENG, Shigeki SUGIMOTO, Masatoshi OKUTOMI, },
journal={IEICE TRANSACTIONS on Information},
title={ASPnP: An Accurate and Scalable Solution to the Perspective-n-Point Problem},
year={2013},
volume={E96-D},
number={7},
pages={1525-1535},
abstract={We propose an accurate and scalable solution to the perspective-n-point problem, referred to as ASPnP. Our main idea is to estimate the orientation and position parameters by directly minimizing a properly defined algebraic error. By using a novel quaternion representation of the rotation, our solution is immune to any parametrization degeneracy. To obtain the global optimum, we use the Grobner basis technique to solve the polynomial system derived from the first-order optimality condition. The main advantages of our proposed solution lie in accuracy and scalability. Extensive experiment results, with both synthetic and real data, demonstrate that our proposed solution has better accuracy than the state-of-the-art noniterative solutions. More importantly, by exploiting vectorization operations, the computational cost of our ASPnP solution is almost constant, independent of the number of point correspondences n in the wide range from 4 to 1000. In our experiment settings, the ASPnP solution takes about 4 milliseconds, thus best suited for real-time applications with a drastically varying number of 3D-to-2D point correspondences.},
keywords={},
doi={10.1587/transinf.E96.D.1525},
ISSN={1745-1361},
month={July},}

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TY - JOUR
TI - ASPnP: An Accurate and Scalable Solution to the Perspective-n-Point Problem
T2 - IEICE TRANSACTIONS on Information
SP - 1525
EP - 1535
AU - Yinqiang ZHENG
AU - Shigeki SUGIMOTO
AU - Masatoshi OKUTOMI
PY - 2013
DO - 10.1587/transinf.E96.D.1525
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2013
AB - We propose an accurate and scalable solution to the perspective-n-point problem, referred to as ASPnP. Our main idea is to estimate the orientation and position parameters by directly minimizing a properly defined algebraic error. By using a novel quaternion representation of the rotation, our solution is immune to any parametrization degeneracy. To obtain the global optimum, we use the Grobner basis technique to solve the polynomial system derived from the first-order optimality condition. The main advantages of our proposed solution lie in accuracy and scalability. Extensive experiment results, with both synthetic and real data, demonstrate that our proposed solution has better accuracy than the state-of-the-art noniterative solutions. More importantly, by exploiting vectorization operations, the computational cost of our ASPnP solution is almost constant, independent of the number of point correspondences n in the wide range from 4 to 1000. In our experiment settings, the ASPnP solution takes about 4 milliseconds, thus best suited for real-time applications with a drastically varying number of 3D-to-2D point correspondences.
ER -