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

Kangni Kueviakoe,Alain Lambert,Zhan Wang,Emmanuelle Frenoux,Philippe Tarroux

doi:10.1155/2018/3769058

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.

Highlights

This paper deals with outdoor vehicle ego-localization
We propose to define the localization problem as a dynamic interval constraint satisfaction problem: at time k, a new ICSP k (Vk, Dk, Ck) is generated and defined as follows: (i) a set of variables, Vk, Vk = xk, yk, θk, xk−1, yk−1, θk−1, δsk, δθk, (ii) a set of domains, Dk, Dk = xk, yk, θk, xk−1, yk−1, θk−1, δsk, δθk, (iii) a set of constraints, Ck, Ck = C1k, C2k, C3k T, C1k⇔ xk = xk−1 + δsk cos θk−1
The building and the solving of the multiple dynamic interval constraint satisfaction problem are realized by our proposed dynamic interval constraint propagation with the splitting algorithm (DICSP, see Algorithm 1), explained hereafter: (i) Line 1: the oldest variable θk−Ξ, representing the orientation in the initial Dynamic Interval Constraint Satisfaction Problem (DICSP), is split into ν intervals

Summary

Introduction

This paper deals with outdoor vehicle ego-localization. Localization is part of many automotive applications where safety is of crucial importance. Interval constraint propagation (ICP) was used to solve the robotic localization problems by Gning and Bonnifait [6, 24] more than ten years ago. It was mainly used for outdoor vehicle localization [25, 26] and underwater robot localization [3, 5]. Those works use a forward-backward propagation technique based on primitive constraints (following the principle of the Waltz algorithm [27]). We improved previous works by proposing a new ICP method for vehicle localization with heading correction and real-time capabilities. A new localization problem formalization and its solving are presented in Section 4 while Section 5 shows our experimental results

Overview of Interval Analysis and Constraint Propagation

ICP Algorithms

The Localization Problem

Results

Conclusion