A robust method for computing vehicle ego-motion

G.P. Stein; O. Mano; A. Shashua

doi:10.1109/IVS.2000.898370

A robust method for computing vehicle ego-motion

G.P. Stein, O. Mano, A. Shashua

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Contribution to conference › Paper › peer-review

115 Scopus citations

Abstract

We describe a robust method for computing the ego-motion of the vehicle relative to the road using input from a single camera mounted next to the rear view mirror. Since feature points are unreliable in cluttered scenes we use direct methods where image values in the two images are combined in a global probability function. Combined with the use of probability distribution matrices, this enables the formulation of a robust method that can ignore large number of outliers as one would encounter in real traffic situations. The method has been tested in real world environments and has been shown to be robust to glare, rain and moving objects in the scene.

Original language	English
Pages	362-368
Number of pages	7
DOIs	https://doi.org/10.1109/IVS.2000.898370
State	Published - 2000
Event	IEEE Intelligent Vehicles Symposium 2000 - The Ritz-Carlton Hotel, Dearborn, United States Duration: 3 Oct 2000 → 3 Oct 2000 https://ieeexplore.ieee.org/xpl/conhome/7217/proceeding

Conference

Conference	IEEE Intelligent Vehicles Symposium 2000
Country/Territory	United States
City	Dearborn
Period	3/10/00 → 3/10/00
Internet address	https://ieeexplore.ieee.org/xpl/conhome/7217/proceeding

Keywords

computation
Intelligent Vehicles
Image processing and computer vision

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10.1109/IVS.2000.898370License: Unspecified

Cite this

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AB - We describe a robust method for computing the ego-motion of the vehicle relative to the road using input from a single camera mounted next to the rear view mirror. Since feature points are unreliable in cluttered scenes we use direct methods where image values in the two images are combined in a global probability function. Combined with the use of probability distribution matrices, this enables the formulation of a robust method that can ignore large number of outliers as one would encounter in real traffic situations. The method has been tested in real world environments and has been shown to be robust to glare, rain and moving objects in the scene.

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KW - Image processing and computer vision

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A robust method for computing vehicle ego-motion

Abstract

Conference

Keywords

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