Computer vision plays an important role in many fields these days. From robotics to biomedical equipment to the car industry to the semi-conductor industry, many applications have been developed for solving problems using visual information. One computer vision application in robotics is a camera-based sensor mounted on a mobile robot vehicle. Since the late 1960s, this system has been utilized in various fields, such as automated warehouses, unmanned ground vehicles, space robots, and driver assistance systems. Each system has a different mission, like terrain analysis and evaluation, visual odometers, lane departure warning systems, and identification of such moving object as other cars and pedestrians. Thus, various features and methods have been applied and tested to solve different computer vision tasks.

A main goal of this vision sensor for an autonomous ground vehicle is to provide such continuous and precise perception information as traversable paths, future trajectory estimations, and lateral position error corrections with small data size. To accomplish these objectives, multi-camera-based Path Finder and Lane Finder Smart Sensors were developed and utilized on an autonomous vehicle at the University of Florida’s Center for Intelligent Machines and Robotics (CIMAR). These systems create traversable area information for both an unstructured road environment and an urban environment in real time.

Extracted traversable information is provided to the robot’s intelligent system and control system in vector data form through the Joint Architecture for Unmanned Systems (JAUS) protocol. Moreover, a small data size is used to represent the real world and its properties. Since vector data are small enough for storing, retrieving, and communication, traversability data and its properties are stored at the World Model Vector Knowledge Store for future reference. (Full text of this dissertation may be available via the University of Florida Libraries web site. Please check http://www.uflib.ufl.edu/etd.html)