A DAISY-CHAINING APPROACH FOR VISION-BASED CONTROL AND ESTIMATION By SIDDHARTHA S. MEHTA A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy A DAISY-CHAINING APPROACH FOR VISION-BASED CONTROL AND ESTIMATION By Siddhartha S. Mehta April 2010 Chair: Dr. Warren E. Dixon Co-Chair: Dr. Prabir Barooah Major: Mechanical Engineering The research presented in this dissertation monograph lies within the general scope of guidance, navigation, and control of autonomous systems and centers around the design and analysis of visual servo control strategies and vision-based robust position and orientation (i.e., pose) estimation. The motivation behind the presented research is to enable a vision system to provide robust navigation and control of autonomous agents operating over a large area. In order to enable vision systems to provide pose estimates over a large area, a new daisy-chaining method is developed. By developing multi-view geometry, or photogrammetry, based concepts relationships are established between the current pose of an agent and the desired agent pose, when the desired agent is out of the camera field-of-view (FOV). The daisy-chaining method is limited by the need to maintain a single reference object that is contained in both the current view and the final view of the desired pose of the vehicle. To overcome this limitation, the daisy-chaining method is extended to allow multiple reference objects to enter and leave the camera FOV, allowing theoretically infinite daisy-chaining and hence an unrestricted applicative area for an UGV. Error propagation analysis for the daisy-chaining method, which resembles a ‘dead-reckoning’ scheme, shows the methods is susceptible to image noise and feature point outliers. To address the local pose estimation problem, a statistical method is