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Map-less long-term localization in complex industrial environments

Map-less long-term localization in complex industrial environments This paper aims to study the localization problem for autonomous industrial vehicles in the complex industrial environments. Aiming for practical applications, the pursuit is to build a map-less localization system which can be used in the presence of dynamic obstacles, short-term and long-term environment changes.Design/methodology/approachThe proposed system contains four main modules, including long-term place graph updating, global localization and re-localization, location tracking and pose registration. The first two modules fully exploit the deep-learning based three-dimensional point cloud learning techniques to achieve the map-less global localization task in large-scale environment. The location tracking module implements the particle filter framework with a newly designed perception model to track the vehicle location during movements. Finally, the pose registration module uses visual information to exclude the influence of dynamic obstacles and short-term changes and further introduces point cloud registration network to estimate the accurate vehicle pose.FindingsComprehensive experiments in real industrial environments demonstrate the effectiveness, robustness and practical applicability of the map-less localization approach.Practical implicationsThis paper provides comprehensive experiments in real industrial environments.Originality/valueThe system can be used in the practical automated industrial vehicles for long-term localization tasks. The dynamic objects, short-/long-term environment changes and hardware limitations of industrial vehicles are all considered in the system design. Thus, this work moves a big step toward achieving real implementations of the autonomous localization in practical industrial scenarios. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Assembly Automation Emerald Publishing

Map-less long-term localization in complex industrial environments

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References (40)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0144-5154
DOI
10.1108/aa-06-2021-0088
Publisher site
See Article on Publisher Site

Abstract

This paper aims to study the localization problem for autonomous industrial vehicles in the complex industrial environments. Aiming for practical applications, the pursuit is to build a map-less localization system which can be used in the presence of dynamic obstacles, short-term and long-term environment changes.Design/methodology/approachThe proposed system contains four main modules, including long-term place graph updating, global localization and re-localization, location tracking and pose registration. The first two modules fully exploit the deep-learning based three-dimensional point cloud learning techniques to achieve the map-less global localization task in large-scale environment. The location tracking module implements the particle filter framework with a newly designed perception model to track the vehicle location during movements. Finally, the pose registration module uses visual information to exclude the influence of dynamic obstacles and short-term changes and further introduces point cloud registration network to estimate the accurate vehicle pose.FindingsComprehensive experiments in real industrial environments demonstrate the effectiveness, robustness and practical applicability of the map-less localization approach.Practical implicationsThis paper provides comprehensive experiments in real industrial environments.Originality/valueThe system can be used in the practical automated industrial vehicles for long-term localization tasks. The dynamic objects, short-/long-term environment changes and hardware limitations of industrial vehicles are all considered in the system design. Thus, this work moves a big step toward achieving real implementations of the autonomous localization in practical industrial scenarios.

Journal

Assembly AutomationEmerald Publishing

Published: Nov 24, 2021

Keywords: Autonomous robots; Automated vehicles; Localization in industrial environments; Long-term localization; Map-less localization

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