Pose tracking of multiple camera system.

Leung, Man Kin.Thesis submitted in: October 2008.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (leaves 121-126).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Motivation --- p.4Chapter 1.3 --- Contributions --- p.5Chapter 1.4 --- Organization of the thesis --- p.6Chapter 2 --- Literature review --- p.8Chapter 2.1 --- Introduction --- p.8Chapter 2.2 --- Background knowledge --- p.9Chapter 2.2.1 --- Pinhole camera model --- p.10Chapter 2.2.2 --- Kalman filter --- p.11Chapter 2.2.3 --- Extended Kalman filter --- p.14Chapter 2.2.4 --- Unscented Kalman filter --- p.15Chapter 2.3 --- Batch method --- p.19Chapter 2.3.1 --- Multiple view geometry --- p.19Chapter 2.3.2 --- Factorization --- p.21Chapter 2.3.3 --- Bundle adjustment --- p.22Chapter 2.4 --- Sequential method --- p.23Chapter 2.5 --- SLAM using cameras --- p.24Chapter 2.6 --- Summary --- p.26Chapter 3 --- Pose tracking of a stereo camera system --- p.27Chapter 3.1 --- Overview --- p.27Chapter 3.1.1 --- Related work --- p.27Chapter 3.1.2 --- Contribution --- p.29Chapter 3.2 --- Problem definition --- p.29Chapter 3.3 --- Algorithm --- p.31Chapter 3.3.1 --- Initialization --- p.33Chapter 3.3.2 --- Feature tracking and stereo correspondence matching --- p.33Chapter 3.3.3 --- Pose tracking based on two trifocal tensors --- p.35Chapter 3.3.4 --- Pose tracking using extended Kalman filter (Our EKF-2 approach) --- p.37Chapter 3.3.5 --- Pose tracking using unscented Kalman filter (Our UKF-2 approach) --- p.41Chapter 3.3.6 --- Pose tracking using differential evolution (Our DE-2 approach) --- p.44Chapter 3.4 --- Experiment --- p.49Chapter 3.4.1 --- Synthetic experiments --- p.49Chapter 3.4.2 --- Real experiments --- p.55Chapter 3.5 --- Summary --- p.67Chapter 4 --- Advance to two pairs of stereo cameras --- p.68Chapter 4.1 --- Overview --- p.68Chapter 4.1.1 --- Related work --- p.68Chapter 4.1.2 --- Contribution --- p.69Chapter 4.2 --- Problem definition --- p.70Chapter 4.3 --- Algorithm --- p.72Chapter 4.3.1 --- Initialization --- p.72Chapter 4.3.2 --- Feature tracking and stereo correspondence matching --- p.74Chapter 4.3.3 --- Pose tracking based on four trifocal tensors --- p.76Chapter 4.3.4 --- Pose tracking using extended Kalman filter (Our EKF-4 approach) --- p.79Chapter 4.3.5 --- Pose tracking using unscented Kalman filter (Our UKF-4 approach) --- p.84Chapter 4.4 --- Experiment --- p.87Chapter 4.4.1 --- Synthetic experiments --- p.87Chapter 4.4.2 --- Real experiments --- p.100Chapter 4.5 --- Summary --- p.113Chapter 5 --- Conclusion --- p.115Chapter 5.1 --- Conclusion --- p.115Chapter 5.2 --- Scope of Applications --- p.116Chapter 5.3 --- Limitations --- p.117Chapter 5.4 --- Difficulties --- p.118Chapter 5.5 --- Future work --- p.118Bibliography --- p.12

Relative Pose Estimation Using Non-overlapping Multicamera Clusters

This thesis considers the Simultaneous Localization and Mapping (SLAM) problem using a set of perspective cameras arranged such that there is no overlap in their fields-of-view. With the known and fixed extrinsic calibration of each camera within the cluster, a novel real-time pose estimation system is presented that is able to accurately track the motion of a camera cluster relative to an unknown target object or environment and concurrently generate a model of the structure, using only image-space measurements. A new parameterization for point feature position using a spherical coordinate update is presented which isolates system parameters dependent on global scale, allowing the shape parameters of the system to converge despite the scale parameters remaining uncertain. Furthermore, a flexible initialization scheme is proposed which allows the optimization to converge accurately using only the measurements from the cameras at the first time step. An analysis is presented identifying the configurations of the cluster motions and target structure geometry for which the optimization solution becomes degenerate and the global scale is ambiguous. Results are presented that not only confirm the previously known critical motions for a two-camera cluster, but also provide a complete description of the degeneracies related to the point feature constellations. The proposed algorithms are implemented and verified in experiments with a camera cluster constructed using multiple perspective cameras mounted on a quadrotor vehicle and augmented with tracking markers to collect high-precision ground-truth motion measurements from an optical indoor positioning system. The accuracy and performance of the proposed pose estimation system are confirmed for various motion profiles in both indoor and challenging outdoor environments