As the mission field for autonomous vehicles expands into a larger variety of territories, the development of autonomous surface vehicles (ASVs) becomes increasingly important. ASVs have the potential to travel for long periods of time in areas that cannot be reached by aerial, ground, or underwater autonomous vehicles. ASVs are useful for a variety of missions, including bathymetric mapping, communication with other autonomous vehicles, military reconnaissance and surveillance, and environmental data collecting. Critical to an ASV's ability to maneuver without human intervention is its ability to detect obstacles, including the shoreline. Prior topological knowledge of the environment is not always available or, in dynamic environments, reliable. While many existing obstacle detection systems can only detect 3D obstacles at close range via a laser or radar signal, vision systems have the potential to detect obstacles both near and far, including "flat" obstacles such as the shoreline. The challenge lies in processing the images acquired by the vision system and extracting useful information. While this thesis does not address the issue of processing the images to locate the pixel positions of the obstacles, we assume that we have these processed images available. We present an algorithm that takes these processed images and, by incorporating the kinematic model of the ASV, maps the pixel locations of the obstacles into a global coordinate system. An Extended Kalman Filter is used to localize the ASV and the surrounding obstacles. / Master of Science

Omni-directional (O-D) infrared (IR) vision is an effective capability for mobile systems in robotics, due to its advantages: illumination invariance, wide field-of-view, ease of identifying heat-emitting objects, and long term tracking without interruption. Unfortunately, O-D IR sensors have low resolution, low frame rates, high cost, sensor noise, and an increase in tracking time. In order to overcome these disadvantages, we propose an autonomous system application in indoor scenarios including 1) Dynamic 3D Reconstruction (D3DR) of the target view in real time images, 2) Human Behavior-based Target Tracking from O-D thermal images, 3) Thermal Multisensor Fusion (TMF), and 4) Visual Perception for Social Cognition from the motion behavior of the human target.