Estimation and prediction of the vehicle's motion based on visual odometry and kalman filter

  • Authors:

  • Basam Musleh;David Martin;Arturo de la Escalera;Domingo Miguel Guinea;Maria Carmen Garcia-Alegre

  • Affiliations:

  • Intelligent Systems Lab, University Carlos III, Leganes, Madrid, Spain;Intelligent Systems Lab, University Carlos III, Leganes, Madrid, Spain;Intelligent Systems Lab, University Carlos III, Leganes, Madrid, Spain;Intelligent Systems Lab, University Carlos III, Leganes, Madrid, Spain;Center for Automation and Robotics (CAR), Spanish Council for Scientific Research (CSIC), Arganda del Rey., Madrid, Spain

  • Venue:
  • ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
  • Year:
  • 2012

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Abstract

The movement of the vehicle is an useful information for different applications, such as driver assistant systems or autonomous vehicles. This information can be known by different methods, for instance, by using a GPS or by means of the visual odometry. However, there are some situations where both methods do not work correctly. For example, there are areas in urban environments where the signal of the GPS is not available, as tunnels or streets with high buildings. On the other hand, the algorithms of computer vision are affected by outdoor environments, and the main source of difficulties is the variation in the ligthing conditions. A method to estimate and predict the movement of the vehicle based on visual odometry and Kalman filter is explained in this paper. The Kalman filter allows both filtering and prediction of vehicle motion, using the results from the visual odometry estimation.