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Journal of Sensors
Volume 2018, Article ID 3769058, 12 pages
https://doi.org/10.1155/2018/3769058
Research Article

Localization of a Vehicle: A Dynamic Interval Constraint Satisfaction Problem-Based Approach

1LIMSI, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91403 Orsay, France
2LRI, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91403 Orsay, France

Correspondence should be addressed to Alain Lambert; rf.dusp-u@trebmal.niala

Received 13 September 2017; Revised 23 January 2018; Accepted 8 February 2018; Published 10 April 2018

Academic Editor: Oleg Lupan

Copyright © 2018 Kangni Kueviakoe et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper introduces a new interval constraint propagation (ICP) approach dealing with the real-time vehicle localization problem. Bayesian methods like extended Kalman filter (EKF) are classically used to achieve vehicle localization. ICP is an alternative which provides guaranteed localization results rather than probabilities. Our approach assumes that all models and measurement errors are bounded within known limits without any other hypotheses on the probability distribution. The proposed algorithm uses a low-level consistency algorithm and has been validated with an outdoor vehicle equipped with a GPS receiver, a gyro, and odometers. Results have been compared to EKF and other ICP methods such as hull consistency (HC4) and 3-bound (3B) algorithms. Both consistencies of EKF and our algorithm have been experimentally studied.