Mapping & localization for use in autonomous quay crane research - ZYC
Autonomous quay cranes (AQCs) are revolutionizing port operations by automating container handling tasks. Precise localization of the AQC's trolley is vital for safe and efficient container handling. This paper investigates the potential of Simultaneous Localization and Mapping (SLAM) algori...
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sg-ntu-dr.10356-1769072024-05-24T15:43:37Z Mapping & localization for use in autonomous quay crane research - ZYC Hilmie Bin Wazit Wang Dan Wei School of Electrical and Electronic Engineering EDWWANG@ntu.edu.sg Engineering Autonomous quay cranes (AQCs) are revolutionizing port operations by automating container handling tasks. Precise localization of the AQC's trolley is vital for safe and efficient container handling. This paper investigates the potential of Simultaneous Localization and Mapping (SLAM) algorithms for AQC mapping and localization. A comparative analysis of several SLAM algorithms was performed and evaluated. The study delineates its examination into three distinct facets within the context of AQC environments: real-time efficacy, precision, and robustness. We utilize a simulation environment built with Gazebo and Rviz to evaluate the performance of these algorithms. The chosen sensor suite includes LiDAR and IMU considering factors like accuracy, robustness, range. Our analysis focuses on how well each SLAM algorithm will simulate an AQC dynamic environment such as dynamic objects. The findings from this study will contribute to the development of robust and reliable AQC localization systems using SLAM for improved efficiency and safety in port operations. Bachelor's degree 2024-05-23T05:00:49Z 2024-05-23T05:00:49Z 2024 Final Year Project (FYP) Hilmie Bin Wazit (2024). Mapping & localization for use in autonomous quay crane research - ZYC. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176907 https://hdl.handle.net/10356/176907 en A1167-231 application/pdf Nanyang Technological University |
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Autonomous quay cranes (AQCs) are revolutionizing port operations by automating
container handling tasks. Precise localization of the AQC's trolley is vital for safe and
efficient container handling. This paper investigates the potential of Simultaneous
Localization and Mapping (SLAM) algorithms for AQC mapping and localization. A
comparative analysis of several SLAM algorithms was performed and evaluated. The
study delineates its examination into three distinct facets within the context of AQC
environments: real-time efficacy, precision, and robustness. We utilize a simulation
environment built with Gazebo and Rviz to evaluate the performance of these
algorithms. The chosen sensor suite includes LiDAR and IMU considering factors like
accuracy, robustness, range. Our analysis focuses on how well each SLAM algorithm
will simulate an AQC dynamic environment such as dynamic objects. The findings
from this study will contribute to the development of robust and reliable AQC
localization systems using SLAM for improved efficiency and safety in port
operations. |
author2 |
Wang Dan Wei |
author_facet |
Wang Dan Wei Hilmie Bin Wazit |
format |
Final Year Project |
author |
Hilmie Bin Wazit |
author_sort |
Hilmie Bin Wazit |
title |
Mapping & localization for use in autonomous quay crane research - ZYC |
title_short |
Mapping & localization for use in autonomous quay crane research - ZYC |
title_full |
Mapping & localization for use in autonomous quay crane research - ZYC |
title_fullStr |
Mapping & localization for use in autonomous quay crane research - ZYC |
title_full_unstemmed |
Mapping & localization for use in autonomous quay crane research - ZYC |
title_sort |
mapping & localization for use in autonomous quay crane research - zyc |
publisher |
Nanyang Technological University |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/176907 |
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1800916443441659904 |