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Robust robot localization in a complex oil and gas industrial environment
Author(s) -
Merriaux Pierre,
Dupuis Yohan,
Boutteau Rémi,
Vasseur Pascal,
Savatier Xavier
Publication year - 2018
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.21735
Subject(s) - point cloud , particle filter , robot , computer science , lidar , function (biology) , field (mathematics) , monte carlo localization , computer vision , filter (signal processing) , artificial intelligence , real time computing , remote sensing , geography , mathematics , evolutionary biology , pure mathematics , biology
In this paper, we propose a LiDAR‐based robot localization method in a complex oil and gas environment. Localization is achieved in six degrees of freedom (DoF) thanks to a particle filter framework. A new time‐efficient likelihood function, based on a precalculated three‐dimensional likelihood field, is introduced. Experiments are carried out in real environments and their digitized point clouds. Six DoF real‐time localization is achieved with spatial and angular errors of less than 2.5 cm and 1°, respectively, in a real environment of 350m 3 . The proposed approach focuses on real‐time performance on embedded platforms. It enabled the Vikings team to win the first two ARGOS Challenge contests.