Vision and navigation for the Carnegie-Mellon Navlab
01 May 1988-IEEE Transactions on Pattern Analysis and Machine Intelligence (Springer, Berlin, Heidelberg)-Vol. 10, Iss: 3, pp 362-372
TL;DR: A distributed architecture articulated around the CODGER (communication database with geometric reasoning) knowledge database is described for a mobile robot system that includes both perception and navigation tools.
Abstract: A distributed architecture articulated around the CODGER (communication database with geometric reasoning) knowledge database is described for a mobile robot system that includes both perception and navigation tools. Results are described for vision and navigation tests using a mobile testbed that integrates perception and navigation capabilities that are based on two types of vision algorithms: color vision for road following, and 3-D vision for obstacle detection and avoidance. The perception modules are integrated into a system that allows the vehicle to drive continuously in an actual outdoor environment. The resulting system is able to navigate continuously on roads while avoiding obstacles. >
Citations
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01 Jan 1995
TL;DR: It is claimed that the state of computer architecture has been a strong influence on models of thought in Artificial Intelligence over the last thirty years.
Abstract: 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.
1,796Â citations
24 Aug 1991
TL;DR: In this article, the authors make the converse claim that the state of computer architecture has been a strong influence on our models of thought, and they use non-Von Neumann computational models they use share many characteristics with biological computation.
Abstract: 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.
1,537Â citations
TL;DR: The developments of the last 20 years in the area of vision for mobile robot navigation are surveyed and the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment are discussed.
Abstract: 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.
1,386Â citations
TL;DR: Key features of one automated intelligent vehicle/highway system (IVHS) are outlined, it is shown how core driver decisions are improved, and a basic IVHS control system architecture is proposed and a design of some control subsystems is offered.
Abstract: Key features of one automated intelligent vehicle/highway system (IVHS) are outlined, it is shown how core driver decisions are improved, a basic IVHS control system architecture is proposed, and a design of some control subsystems is offered. Some experimental work is summarized. A system that promises a threefold increase in capacity is outlined, and a four-layer hierarchical control architecture that decomposes this problem into more manageable units is proposed. >
1,334Â citations
TL;DR: A review of recent vision-based on-road vehicle detection systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems is presented.
Abstract: Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.
1,181Â citations
References
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14,948Â citations
Book•
01 Jan 1973TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Abstract: Provides a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition. The topics treated include Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
13,647Â citations
Book•
01 Jun 1991TL;DR: A new architecture for controlling mobile robots is described, building a robust and flexible robot control system that has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms.
Abstract: A new architecture for controlling mobile robots is described. Layers of control system are built to let the robot operate at increasing levels of competence. Layers are made up of asynchronous modules that communicate over low-bandwidth channels. Each module is an instance of a fairly simple computational machine. Higher-level layers can subsume the roles of lower levels by suppressing their outputs. However, lower levels continue to function as higher levels are added. The result is a robust and flexible robot control system. The system has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms. Eventually it is intended to control a robot that wanders the office areas of our laboratory, building maps of its surroundings using an onboard arm to perform simple tasks.
7,759Â citations
01 Mar 1986
TL;DR: In this paper, a new architecture for controlling mobile robots is described, which is made up of asynchronous modules that communicate over low-bandwidth channels, each module is an instance of a fairly simple computational machine.
Abstract: A new architecture for controlling mobile robots is described. Layers of control system are built to let the robot operate at increasing levels of competence. Layers are made up of asynchronous modules that communicate over low-bandwidth channels. Each module is an instance of a fairly simple computational machine. Higher-level layers can subsume the roles of lower levels by suppressing their outputs. However, lower levels continue to function as higher levels are added. The result is a robust and flexible robot control system. The system has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms. Eventually it is intended to control a robot that wanders the office areas of our laboratory, building maps of its surroundings using an onboard arm to perform simple tasks.
7,291Â citations
A M Waxman1, J. LeMoigne2, Larry S. Davis2, B. Srinivasan2, Todd R. Kushner2, Eli Liang2, T. Siddalingaiah2 •
TL;DR: A modular system architecture has been developed to support visual navigation by an autonomous land vehicle that consists of vision modules performing image processing, three-dimensional shape recovery, and geometric reasoning, as well as modules for planning, navigating, and piloting.
Abstract: A modular system architecture has been developed to support visual navigation by an autonomous land vehicle. The system consists of vision modules performing image processing, three-dimensional shape recovery, and geometric reasoning, as well as modules for planning, navigating, and piloting. The system runs in two distinct modes, bootstrap and feedforward. The bootstrap mode requires analysis of entire images to find and model the objects of interest in the scene (e.g., roads). In the feedforward mode (while the vehicle is moving), attention is focused on small parts of the visual field as determined by prior views of the scene, to continue to track and model the objects of interest. General navigational tasks are decomposed into three categories, all of which contribute to planning a vehicle path. They are called long-, intermediate-, and short-range navigation, reflecting the scale to which they apply. The system has been implemented as a set of concurrent communicating modules and used to drive a camera (carried by a robot arm) over a scale model road network on a terrain board. A large subset of the system has been reimplemented on a VICOM image processor and has driven the DARPA Autonomous Land Vehicle (ALV) at Martin Marietta's test site in Denver, CO.
257Â citations