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Journal ArticleDOI

Vision and navigation for the Carnegie-Mellon Navlab

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TLDR
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. >

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Citations
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Journal ArticleDOI

Road Geometry Classification by Adaptive Shape Models

TL;DR: The aim of this paper is to classify road geometries for road detection through the analysis of scene composition and temporal coherence, and it is shown that invariant features and temporal information provide robustness against disturbing imaging conditions.
Proceedings ArticleDOI

Outdoor vehicle navigation using passive 3D vision

TL;DR: A prototype 3D vision system which uses the structure-from-motion principle, based on the extraction and matching of image tokens such as edges and corners, indicates that the inferred surfaces can be readily segmented into navigable and nonnavigable regions.
Journal ArticleDOI

Autonomous robotic inspection and manipulation using multisensor feedback

TL;DR: A six-degree-of-freedom industrial robot to which was added a number of sensors-vision, range, sound, proximity, force/torque, and touch-to enhance its inspection and manipulation capabilities is described, demonstrating the need for multisensory information to accomplish complex, autonomous robotic Inspection and manipulation tasks.
Journal ArticleDOI

Vision-guided navigation using SHOSLIF

TL;DR: This paper presents an unconventional approach to vision-guided autonomous navigation that combines principle component and linear discriminant analysis networks with a decision tree network and has been tested on a mobile robot in an unknown indoor environment to learn scenes and the associated navigation experience.
Book ChapterDOI

Conceptual Bases of Robot Navigation Modeling, Control and Applications

TL;DR: This chapter presents and discusses the main topics involved on the design or adoption of a mobile robot system and focus on the control and navigation systems for autonomous mobile robots.
References
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Book

Pattern classification and scene analysis

TL;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.
Book

A robust layered control system for a mobile robot

TL;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.
Journal ArticleDOI

A robust layered control system for a mobile robot

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.
Journal ArticleDOI

A visual navigation system for autonomous land vehicles

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.
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