Outlier rejection by oriented tracks to aid pose estimation from video

Authors:
Brad Grinstead;Andreas Koschan;Andrei Gribok;Mongi A. Abidi;David Gorsich
Affiliations:
Imaging, Robotics, and Intelligent Systems Laboratory, Department of Electrical and Computer Engineering, The University of Tennessee, 334 Ferris Hall, Knoxville, TN 37996, United States;Imaging, Robotics, and Intelligent Systems Laboratory, Department of Electrical and Computer Engineering, The University of Tennessee, 334 Ferris Hall, Knoxville, TN 37996, United States;Imaging, Robotics, and Intelligent Systems Laboratory, Department of Electrical and Computer Engineering, The University of Tennessee, 334 Ferris Hall, Knoxville, TN 37996, United States;Imaging, Robotics, and Intelligent Systems Laboratory, Department of Electrical and Computer Engineering, The University of Tennessee, 334 Ferris Hall, Knoxville, TN 37996, United States;U.S. Army RDECOM Tank-Automotive Research, Development and Engineering Center, Warren, MI 48397-5000, USA
Venue:
Pattern Recognition Letters
Year:
2006

Citing 12
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Outlier rejection for cameras on intelligent vehicles

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Abstract

This paper introduces a method for rejecting the false matches of points between successive views in a video sequence used to perform Pose from Motion for a mobile sensing platform. Typical methods for pose estimation require point correspondences to estimate the epipolar geometry between the two views. Algorithms for determining these correspondences invariably output false matches along with the good. We present an algorithm for identifying and removing these mismatches for scenes generated by a mobile scanning platform. The algorithm utilizes the motion characteristics of a rear-wheel drive sensing platform to identify correct point matches through their common motion trajectories. Our algorithm works in cases where the percentage of false matches may be as high as 80%, providing a set of correspondences whose correct/incorrect match ratio is higher than the mutual best match approach found in the literature. This algorithm is intended as a post-processing step for any point correspondence algorithm and its output can be used in standard pose estimation algorithms to enhance their speed and accuracy. Experimental results show the computational savings of our approach over the mutual best match method, resulting in comparable or better outlier rejection-increasing the true/false match ratio by 2-3 times-in only a fraction of the time.