Model Based Pose Estimator Using Linear-Programming

Given a ? object and some measurements for points in this object, it is desired to find the ? location of the object. A new model based pose estimator from stereo pairs based on linear programming (LP) is presented. In the presence of outliers, the new LP estimator provides better results than maximum likelihood estimators such as weighted least squares, and is usually almost as good as robust estimators such as LMEDS. In the presence of noise the new LP estimator provides better results than robust estimators such as LMEDS, and is slightly inferior to maximum likelihood estimators such as weighted least squares. In the presence of noise and outliers - especially for wide angle stereo - the new estimator provides the best results. The LP estimator is based on correspondence of a points to convex polyhedrons. Each points corresponds to a unique polyhedron, which represents its uncertainty in ? as computed from the stereo pair. Polyhedron can also be computed for ? data point by using a...

• Type: Conference
• Year: 2000
• Where: ECCV
• Authors: Moshe Ben-Ezra, Shmuel Peleg, Michael Werman
• Keywords: Computer Vision, ECCV 2000, Maximum Likelihood Estimators, Outlier Rejection Phase, Pose Estimator, Robust Estimators, Wide Angle Stereo