Computers and Thought are the two categories that together define Artificial Intelligence as a discipline. It is generally accepted that work in Artificial Intelligence over the last thirty years has had a strong influence on aspects of computer architectures. In this paper we also make the converse claim; that the state of computer architecture has been a strong influence on our models of thought. The Von Neumann model of computation has lead Artificial Intelligence in particular directions. Intelligence in biological systems is completely different. Recent work in behavior-based Artificial Intelligence has produced new models of intelligence that are much closer in spirit to biological systems. The non-Von Neumann computational models they use share many characteristics with biological computation.

Intelligence without reason.

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Intelligence without reason

Surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment.

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Vision for mobile robot navigation: a survey

An imaging system for a vehicle includes an imaging device having a field of view exteriorly and forward of the vehicle in its direction of travel, and an image processor operable to process the captured images in accordance with an algorithm. The algorithm comprises a sign recognition routine and a character recognition routine. The image processor processes the image data captured by the imaging device to detect signs in the field of view of the imaging device and applies the sign recognition routine to determine a sign type of the detected sign. The image processor is operable to apply the character recognition routine to the image data to determine information on the detected sign. The image processor applies the character recognition routine to the captured images in response to an output of the sign recognition routine being indicative of the detected sign being a sign type of interest.

Imaging system for vehicle

The issues involved in integrating multiple sensors into the operation of a system are presented in the context of the type of information these sensors can uniquely provide. A survey is provided of the variety of approaches to the problem of multisensor integration and fusion that have appeared in the literature in recent years ranging from general paradigms, frameworks, and methods for integrating and fusing multisensory information to existing multisensor systems used in different areas of application. General multisensor fusion methods, sensor selection strategies, and world models are examined, along with approaches to the integration and fusion of information from combinations of different types of sensors. Short descriptions of the role of multisensor integration and fusion in the operation of a number of existing mobile robots are provided, together with proposed high-level multisensory representations suitable for mobile robot navigation and control. Existing multisensor systems for industrial and other applications are considered. >

Multisensor integration and fusion in intelligent systems

A description is given of VITS (for vision task sequencer), the vision system for the autonomous land vehicle (ALV) Alvin, addressing in particular the task of road-following. The ALV vision system builds symbolic descriptions of road and obstacle boundaries using both video and range sensors. The authors discuss various road segmentation methods for video-based road-following, along with approaches to boundary extraction and transformation of boundaries in the image plane into a vehicle-centered three-dimensional scene model. >

VITS-a vision system for autonomous land vehicle navigation

We describe the vision system for Alvin, the Autonomous Land Vehicle, addressing in particular the task of road-following. The system builds symbolic descriptions of the road and obstacle boundaries using both video and range sensors. Road segmentation methods are described for video-based road-following, along with approaches to boundary extraction and the transformation of boundaries in the image plane into a vehicle-centered three dimensional scene model. The ALV has performed public road-following demonstrations, traveling distances up to 4.5 km at speeds up to 20 km/hr along a paved road, equipped with an RGB video camera with pan/tilt control and a laser range scanner.

D.G. Morgenthaler

Papers

VITS-a vision system for autonomous land vehicle navigation

Video road-following for the autonomous land vehicle