| Summary: | 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.
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