Titre :

Informational Framework for Minimalistic Visual Odometry on Outdoor Robot

Auteur(s) :

Al Hage, Joelle [Auteur]
Heuristique et Diagnostic des Systèmes Complexes [Compiègne] [Heudiasyc]
Mafrica, Stefano [Auteur]
Institut des Sciences du Mouvement Etienne Jules Marey [ISM]
El Badaoui El Najjar, Maan [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Ruffier, Franck [Auteur]
Biorobotique [BIOROB]

Titre de la revue :

IEEE Transactions on Instrumentation and Measurement

Pagination :

2988-2995

Éditeur :

Institute of Electrical and Electronics Engineers

Date de publication :

2019-08-01

ISSN :

0018-9456

Mot(s)-clé(s) en anglais :

Optic Flow
Fault Detection and Exclusion
Mobile Robot
Odometry
Information Filter
Data fusion
Kullback-Leibler Divergence

Discipline(s) HAL :

Sciences de l'ingénieur [physics]/Automatique / Robotique

Résumé en anglais : [en]

In an unknown environment, assessing the robot trajectory in real time is one of the key issues for a successful robotic mission. In such environment, the absolute measurements like the GPS data may be unavailable. Moreover, ...
Lire la suite >In an unknown environment, assessing the robot trajectory in real time is one of the key issues for a successful robotic mission. In such environment, the absolute measurements like the GPS data may be unavailable. Moreover, estimating the position using only proprioceptive sensors like encoders and Inertial Measurements Units (IMU) will generate errors that increase over time. This paper presents a multi-sensor fusion approach between IMU and ground Optical Flow (OF) used to estimate the position of a mobile robot while ensuring high integrity localization. The data fusion in done through the informational form of the Kalman Filter namely Information Filter (IF). A Fault Detection and Exclusion (FDE) step is added in order to exclude the erroneous measurements from the fusion procedure by making it fault tolerant and to ensure a high localization performance. The approach is based on the use of the IF for the state estimation and tools from the information theory for the FDE. Our proposed approach evaluates the quality of a measurement based on the amount of information it provides using informational metrics like the Kullback-Leibler divergence. The approach is validated on data obtained from experiments performed in outdoor environments in various conditions including high-dynamic-range lighting and different ground textures.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL