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Robust visual odometry for vehicle localization in urban environments

Published online by Cambridge University Press:  22 May 2009

I. Parra ,
M. A. Sotelo ,
D. F. Llorca  and
M. Ocaña
I. Parra
Affiliation:
Department of Electronics, Escuela Politécnica Superior, University of Alcalá. Alcalá de Henares, Madrid, Spain
M. A. Sotelo*
Affiliation:
Department of Electronics, Escuela Politécnica Superior, University of Alcalá. Alcalá de Henares, Madrid, Spain
D. F. Llorca
Affiliation:
Department of Electronics, Escuela Politécnica Superior, University of Alcalá. Alcalá de Henares, Madrid, Spain
M. Ocaña
Affiliation:
Department of Electronics, Escuela Politécnica Superior, University of Alcalá. Alcalá de Henares, Madrid, Spain
*
*Corresponding author. E-mail: sotelo@depeca.uah.es
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Summary

This paper describes a new approach for estimating the vehicle motion trajectory in complex urban environments by means of visual odometry. A new strategy for robust feature extraction and data post-processing is developed and tested on-road. Images from scale-invariant feature transform (SIFT) features are used in order to cope with the complexity of urban environments. The obtained results are discussed and compared to previous works. In the prototype system, the ego-motion of the vehicle 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. The distance between estimations is dynamically adapted based on re-projection and estimation errors. Vehicle motion is estimated using the non-linear, photogrametric approach based on RAndom SAmple Consensus (RANSAC). The final goal is to provide on-board driver assistance in navigation tasks, or to provide a means of autonomously navigating a vehicle. The method has been tested in real traffic conditions without using prior knowledge about the scene or the vehicle motion. An example of how to estimate a vehicle's trajectory is provided along with suggestions for possible further improvement of the proposed odometry algorithm.

Keywords

3D visual odometryGlobal localizationVisionSIFT

Information

Type
Article
Information
Robotica , Volume 28 , Issue 3 , May 2010 , pp. 441 - 452
Copyright
Copyright © Cambridge University Press 2009

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