Lyapunov-based nonlinear estimation methods with applications to machine vision

Recent advances in image-based information estimation has enabled the use of vision sensor in many robotics and surveillance applications. The work in this dissertation, is focused on developing online techniques for image-based structure and motion (SaM) estimation. Since traditional batch methods are not useful for the online vision-based control tasks, observer-based approaches to the problem have been developed. Starting from the Kalman-filter for SaM problem by L. Matthies, many contributions to the observer approach for the SaM problem exist in literature. Various models are introduced in literature for SaM estimation but two models are prevalent, viz; a kinematic relative motion affine model with implicit outputs and a transformed nonlinear state model with the linear output equation. The existing SaM observers are designed for the case of a stationary object, requires full camera velocity information and cannot be used for certain camera motions. In this dissertation, new solutions to the SaM are presented using the transformed nonlinear state model which can be used for larger set of camera motions, does not require full camera velocity information, and are reduced-order. Solutions for the stationary as well as moving objects viewed by a moving camera are presented.;In Chapter 3, a reduced order observer is developed to estimate the structure of a static object using a moving camera, where full camera velocity and linear acceleration are known. Chapter 4 focuses on the development of a reduced order observer for the SaM estimation of a stationary object when only a single camera linear velocity is known. In Chapter 5, an observer design is presented for a specific class of nonlinear systems where the output dynamics are affine in the unmeasurable state and the dynamics of the unmeasurable state are nonlinear. The method is applied to simultaneously estimate the structure and motion of a moving object seen by a moving camera. Another strategy to the observer design in the presence of an unmeasurable disturbance is an unknown input observer (UIO). Chapter 6 provides a solution to an UIO design for a general class of nonlinear systems and it's application to structure estimation of a moving object is shown in Chapter 7.