Autonomous robotic vehicle road following

Kuan, D.; Phipps, G. S.; Hsueh, A.

doi:10.1109/34.6773

Cited by 96 publications

(33 citation statements)

References 5 publications

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“…In order to classify one pixel as a member of a class "road, " there are a number of possible segmentation methods based on color, texture descriptors based on statistic parameters, structure, or frequency spectrum, etc. While some acceptable results have been obtained when the color components have been used only, even three decades ago [14] or by use of the best candidates among texture statistic and structure descriptors [15], our reasoning here was oriented toward a more complex approach where the color and texture are simultaneously considered [16].…”

Section: Road Region Extractionmentioning

confidence: 99%

A classification of on-road obstacles according to their relative velocities

Bendjaballah

Graovac

Boulahlib

2016

J Image Video Proc.

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The systems based on image processing have numerous applications in the domain of motion control of robots and autonomous vehicles. The current paper is oriented to the solution of the problem that precedes the implementation of automatic avoidance of the on-road obstacles-how to detect them, to track in the sequence of images, and to recognize which of them are stationary, incoming, or outgoing from the camera. The overall algorithm of obstacle classification presented in this paper consists of three basic phases: (1) image segmentation in order to extract the pixels belonging to the image of a road and the objects over it; (2) extraction of characteristic points inside the area of the obstacle, their description and tracking in following frames; and (3) estimation of distances between the camera, the obstacles and their rates of change (relative velocities). The verifications of particular steps of the proposed algorithm are illustrated using real road-traffic images, while the overall algorithm is tested using both synthesized sequences of images and the ones acquired in real driving.

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Section: Road Region Extractionmentioning

confidence: 99%

A classification of on-road obstacles according to their relative velocities

Bendjaballah

Graovac

Boulahlib

2016

J Image Video Proc.

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“…Definitions of construction need to be structured to accurately represent the contemporary construction industry and yet mesh with existing classical (Kuan et al, 1988), and emerging robot architectures "... which typically amalgamates sensing and acting at low levels in the system and combines them synergistically", (Malcolm 1989). …”

Section: The Proposed Communication Systemmentioning

confidence: 99%

A Proposed Language, Syntax and Structure for Communication between the Robotics and Construction Communities

Jones¹,

Cusack²,

Askew³

1992

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Robotics and construction have at least one common characteristic, both are manifestly fragmented and subject to high levels of specialisation. As such, the sources of information and knowledge necessary to bridge the interface between the two communities is difficult to identify, and effecting comprehension of their technologies and cultures is problematic. This paper outlines a collaborative project which seeks to develop a new common language, syntax and structure to enable effective communication between the two communities.

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“…Different algorithms to discriminate between road and non-road regions were proposed using a gray-level or a color camera [1], [2], [3], [4], [5]. However, as noticed by most of these authors, the segmentation of the road surface faces many difficulties which can be grouped in two categories: the spatial and temporal variabilities of the color aspect of the road and of its environment.…”

Section: Introductionmentioning

confidence: 99%

Long-range road detection for off-line scene analysis

Tarel¹,

Bigorgne²

2009

2009 IEEE Intelligent Vehicles Symposium

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Abstract-Long-range detection of road surface in a sequence of images from a front camera aboard a vehicle is known as an unsolved problem. We propose an algorithm using a single camera and based on color segmentation which has interesting performance and which is stable along the sequence whatever its length. It is an off-line algorithm which makes good use of current and successive images to build reliable models of the color aspects of the road and of its environment at each vehicle position. To apply the proposed algorithm a radiometric calibration step is required to ensure uniform responses of the pixels over the image. The algorithm consists in three steps: image smoothing consistent with perspective effect on the road, building of the models of the road and non-road colors, and region growing of the road region. The relevance of the proposed algorithm is illustrated by an application to the roadway visibility estimation in stereovision and its performance are illustrated by experiments in difficult situations.

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Autonomous robotic vehicle road following

Cited by 96 publications

References 5 publications

A classification of on-road obstacles according to their relative velocities

A classification of on-road obstacles according to their relative velocities

A Proposed Language, Syntax and Structure for Communication between the Robotics and Construction Communities

Long-range road detection for off-line scene analysis

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