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Naranjo, J. E.Author

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June 9, 2019
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3D visual odometry for road vehicles

Publicated to: JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS. 51 (1): 113-134 - 2008-01-01 51(1), DOI: 10.1007/s10846-007-9182-5

Authors:

Garcia-Garcia, R; Sotelo, M A; Parra, I; Fernandez, D; Naranjo, J E; Gavilan, M
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Affiliations

CSIC, Ind Automat Inst, Dept Informat, Madrid, Spain - Author
Univ Alcala de Henares, Escuela Politecn Super, Dept Elect, Madrid, Spain - Author

Abstract

This paper describes a method for estimating the vehicle global position in a network of roads by means of visual odometry. To do so, the ego-motion of the vehicle relative to the road is computed using a stereo-vision system mounted next to the rear view mirror of the car. Feature points are matched between pairs of frames and linked into 3D trajectories. Vehicle motion is estimated using the non-linear, photogrametric approach based on RANSAC. This iterative technique enables the formulation of a robust method that can ignore large numbers of outliers as encountered in real traffic scenes. The resulting method is defined as visual odometry and can be used in conjunction with other sensors, such as GPS, to produce accurate estimates of the vehicle global position. The obvious application of the method is to provide on-board driver assistance in navigation tasks, or to provide a means for autonomously navigating a vehicle. The method has been tested in real traffic conditions without using prior knowledge about the scene nor the vehicle motion. We provide examples of estimated vehicle trajectories using the proposed method and discuss the key issues for further improvement.
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Keywords

3d visual odometryEgo-motion estimationNavigation assistanceNon-linear least squaresRansac

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2008, it was in position , thus managing to position itself as a Q2 (Segundo Cuartil), in the category Control and Systems Engineering. Notably, the journal is positioned en el Cuartil Q4 for the agency WoS (JCR) in the category Computer Science, Artificial Intelligence.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-24:

  • WoS: 12
  • Scopus: 25
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-24:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 39 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/2954/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 1,794
  • Downloads: 1,442
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