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Map-less long-term localization in complex industrial environments

Zhe Liu (Department of Computer Science and Technology, University of Cambridge, Cambridge, UK)
Zhijian Qiao (Department of Automation, Shanghai Jiao Tong University, Shanghai, China)
Chuanzhe Suo (Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China)
Yingtian Liu (Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China)
Kefan Jin (MOE Key Laboratory of Marine Intelligent Equipment and System and the State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, China)

Assembly Automation

ISSN: 0144-5154

Article publication date: 4 October 2021

Issue publication date: 24 November 2021




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.


The 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.


Comprehensive experiments in real industrial environments demonstrate the effectiveness, robustness and practical applicability of the map-less localization approach.

Practical implications

This paper provides comprehensive experiments in real industrial environments.


The 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.



Liu, Z., Qiao, Z., Suo, C., Liu, Y. and Jin, K. (2021), "Map-less long-term localization in complex industrial environments", Assembly Automation, Vol. 41 No. 6, pp. 714-724.



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