Mapping & localization for use in autonomous quay crane research - ZYC

Abstract

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.