خط مشی دسترسیدرباره ماپشتیبانی آنلاین
ثبت نامثبت نام
راهنماراهنما
فارسی
ورودورود
صفحه اصلیصفحه اصلی
جستجوی مدارک
تمام متن
منابع دیجیتالی
رکورد قبلیرکورد بعدی
Document Type:Latin Dissertation
Language of Document:English
Record Number:53955
Doc. No:TL23909
Call number:‭3348872‬
Main Entry:Mohammad Ehab Mohammad Ragab
Title & Author:Multiple camera pose estimationMohammad Ehab Mohammad Ragab
College:The Chinese University of Hong Kong (Hong Kong)
Date:2008
Degree:Ph.D.
student score:2008
Page No:185
Abstract:In this thesis, we solve the pose estimation problem for robot motion by placing multiple cameras on the robot. In particular, we use four cameras arranged as two back-to-back stereo pairs combined with the Extended Kalman Filter (EKF). The EKF is used to provide a frame by frame recursive solution suitable for the real-time application at hand. The reason for using multiple cameras is that the pose estimation problem is more constrained for multiple cameras than for a single camera. Their use is further justified by the drop in price which is accompanied by the remarkable increase in accuracy. Back-to-back cameras are used since they are likely to have a larger field of view, provide more information, and capture more features. In this way, they are more likely to disambiguate the pose translation and rotation parameters. Stereo information is used in self-initialization and outlier rejection. Simple yet efficient methods have been proposed to tackle the problem of long-sequence drift. Our approaches have been compared, under different motion patterns, to other methods in the literature which use a single camera. Both the simulations and the real experiments show that our approaches are the most robust and accurate among them all as well as fast enough to realize the real-time requirement of robot navigation. Additionally, we suggest a new formulation for the perspective camera projection matrix. In particular, regarding how the 3 × 3 rotation matrix, R , of the camera should be incorporated into the 3 × 4 camera projection matrix, P . We show that the incorporated rotation should neither be the camera rotation R nor its transpose, but a reversed (left-handed) version of it. The fundamental matrix between a pair of stereo cameras is reformulated more accurately accordingly. This is extremely useful when we want to calculate the fundamental matrix accurately from the stereo camera matrices. It is especially true when the feature correspondences are too few for robust methods, such as RANSAC, to operate. We expect that this new model would have an impact on various applications. Furthermore, the process of estimating the rotation and translation parameters between a stereo pair from the essential matrix is investigated. This is an essential step for our multi-camera pose estimation method. We show that there are 16 solutions based on the singular value decomposition (not four or eight as previously thought). We also suggest a checking step to ascertain that the proposed algorithm will come up with accurate results. The checking step ensures the accuracy of the fundamental matrix calculated using the pose obtained. This provides a speedy way to calibrate a stereo rig. Our proposed theories are supported by the real and synthetic data experiments reported in this thesis.
Subject:Applied sciences; Multiple camera; Pose estimation; Rotation; Bas-relief; Fundamental matrix; Essential matrix; Robot navigation; Robotics; Computer science; 0984:Computer science; 0771:Robotics
Added Entry:K. H. Wong
Added Entry:The Chinese University of Hong Kong (Hong Kong)