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Two Efficient Solutions for Visual Odometry Using Directional Correspondence
April 2012 (vol. 34 no. 4)
pp. 818-824
ASCII Text
x 
 
J. Gallier, X. S. Zhou, O. Naroditsky, S. I. Roumeliotis, K. Daniilidis, "Two Efficient Solutions for Visual Odometry Using Directional Correspondence," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 4, pp. 818-824, April, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2011.226,
author = {J. Gallier and X. S. Zhou and O. Naroditsky and S. I. Roumeliotis and K. Daniilidis},
title = {Two Efficient Solutions for Visual Odometry Using Directional Correspondence},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {34},
number = {4},
issn = {0162-8828},
year = {2012},
pages = {818-824},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.226},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Two Efficient Solutions for Visual Odometry Using Directional Correspondence
IS - 4
SN - 0162-8828
SP818
EP824
EPD - 818-824
A1 - J. Gallier,
A1 - X. S. Zhou,
A1 - O. Naroditsky,
A1 - S. I. Roumeliotis,
A1 - K. Daniilidis,
PY - 2012
KW - vectors
KW - computer vision
KW - distance measurement
KW - geometry
KW - pose estimation
KW - five-point method
KW - visual odometry
KW - directional correspondence
KW - relative pose problem
KW - image point correspondences
KW - reference direction
KW - three-plus-one problem
KW - five-point algorithm
KW - vanishing point
KW - inertial measurement unit
KW - robots
KW - mobile devices
KW - gravity vector
KW - algebraic geometry
KW - RANSAC
KW - four point correspondences
KW - Polynomials
KW - Cameras
KW - Noise
KW - Vectors
KW - Visualization
KW - Sparse matrices
KW - Groebner basis.
KW - Computer vision
KW - structure from motion
KW - visual odometry
KW - minimal problems
VL - 34
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
J. Gallier, Dept. of Comput. & Inf. Sci., Univ. of Pennsylvania, Philadelphia, PA, USA
X. S. Zhou, Dept. of Comput. Sci. & Eng., Univ. of Minnesota, Minneapolis, MN, USA
O. Naroditsky, Dept. of Comput. & Inf. Sci., Univ. of Pennsylvania, Philadelphia, PA, USA
S. I. Roumeliotis, Dept. of Comput. Sci. & Eng., Univ. of Minnesota, Minneapolis, MN, USA
K. Daniilidis, Dept. of Comput. & Inf. Sci., Univ. of Pennsylvania, Philadelphia, PA, USA
This paper presents two new, efficient solutions to the two-view, relative pose problem from three image point correspondences and one common reference direction. This three-plus-one problem can be used either as a substitute for the classic five-point algorithm, using a vanishing point for the reference direction, or to make use of an inertial measurement unit commonly available on robots and mobile devices where the gravity vector becomes the reference direction. We provide a simple, closed-form solution and a solution based on algebraic geometry which offers numerical advantages. In addition, we introduce a new method for computing visual odometry with RANSAC and four point correspondences per hypothesis. In a set of real experiments, we demonstrate the power of our approach by comparing it to the five-point method in a hypothesize-and-test visual odometry setting.

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Index Terms:
vectors,computer vision,distance measurement,geometry,pose estimation,five-point method,visual odometry,directional correspondence,relative pose problem,image point correspondences,reference direction,three-plus-one problem,five-point algorithm,vanishing point,inertial measurement unit,robots,mobile devices,gravity vector,algebraic geometry,RANSAC,four point correspondences,Polynomials,Cameras,Noise,Vectors,Visualization,Sparse matrices,Groebner basis.,Computer vision,structure from motion,visual odometry,minimal problems
Citation:
J. Gallier, X. S. Zhou, O. Naroditsky, S. I. Roumeliotis, K. Daniilidis, "Two Efficient Solutions for Visual Odometry Using Directional Correspondence," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 4, pp. 818-824, April 2012, doi:10.1109/TPAMI.2011.226
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