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Proceedings Paper

Visual road following without 3D reconstruction
Author(s): Martin Herman; Daniel Raviv; Henry Schneiderman; Marilyn Nashman
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Paper Abstract

The traditional approach to visual road following involves reconstructing a 3D model of the road. The model is in a world or vehicle-centered coordinate system, and it is symbolic, iconic, or a combination of both. Road-following commands (as well as other commands, e.g., obstacle avoidance) are then generated from this 3D model. Here we discuss an alternative approach in which a minimal road model is generated. The model contains only task-relevant information and a minimum of vision processing is performed to extract this information in the form of visual cues represented in the 2D image coordinate system. This approach leads to rapid and continuous update of the road model from the visual data. It results in inexpensive, fast, and robust computations. Road following is achieved by servoing on the visual cues in the 2D model. This approach results in a tight coupling of perception and action. In this paper, two specific examples of road following that use this approach are presented. In the first example, we show that road-following commands can be generated from visual cues consisting of the projection into the image of the tangent point on the edge of the road, along with the optical flow of this point. Using this cue, the resulting servo loop is very simple and fast. In the second example, we show that lane markings can be robustly tracked in real time while confining all processing to the 2D image plane. Neither knowledge of vehicle motion nor a calibrated camera is required. This system has been used to drive a vehicle up to 80 km/hr under various road conditions. The algorithm runs at a 15 Hz update rate.

Paper Details

Date Published: 25 February 1994
PDF: 11 pages
Proc. SPIE 2103, 22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs, (25 February 1994); doi: 10.1117/12.169472
Show Author Affiliations
Martin Herman, National Institute of Standards and Technology (United States)
Daniel Raviv, National Institute of Standards and Technology and Florida Atlantic Univ. (United States)
Henry Schneiderman, National Institute of Standards and Technology (United States)
Marilyn Nashman, National Institute of Standards and Technology (United States)


Published in SPIE Proceedings Vol. 2103:
22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs
J. Michael Selander, Editor(s)

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