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<p>Remotely Operated Underwater Vehicles (ROUVs) are increasingly important for high-resolution surveying of narrow shipping lanes. This paper presents 3Clifs, a LiDAR- and AI-enhanced ROUV designed for near-real-time topographic mapping and navigation support in shallow, constrained waterways, demonstrated at three cliff sites in the Suez Canal. The system integrates three-dimensional Light Detection and Ranging (LiDAR) scanning, an Inertial Measurement Unit (IMU), and an onboard processor running the Robot Operating System (ROS) for Simultaneous Localization and Mapping (SLAM). To address data loss from underwater LiDAR (caused by scattering and reflection), we introduce an AI-driven optimisation module that reconstructs missing point cloud data and improves SLAM continuity. We also report propulsion and propeller design changes (propeller v05_1) that reduce flow turbulence and improve scan stability. We compare our approach to sonar-only ROUV mapping and to recent ROUV/LiDAR studies using metrics including point-cloud completeness, SLAM continuity, and navigation-path deviation. The main contributions are: (i) an integrated LiDAR, ROS and AI pipeline for underwater SLAM with missing-point recovery; (ii) a propulsion configuration optimized for LiDAR scanning stability; and (iii) a real-world Suez Canal case study demonstrating practical benefits for narrow-lane navigation.</p>

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Smart ROUV advances for enhanced navigation in the Suez Canal

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月7日

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  • Khaled Oqda,
  • Kareem Mosaad Ibrahim,
  • Ahmed Ibrahim Mustafa,
  • Reem Gamal Allam,
  • Muhamed Ahmed Saad,
  • Ahmed Ibrahim Asl,
  • Mohamed Al-Sayed Abdel Samie,
  • Sara Abdel Fattah Ibrahim,
  • Hassan Yahya Ghanem,
  • Mohamed Mahmoud Habib,
  • Mohamed Ali Metwalliy,
  • Hanaa Salem Marie

摘要

Remotely Operated Underwater Vehicles (ROUVs) are increasingly important for high-resolution surveying of narrow shipping lanes. This paper presents 3Clifs, a LiDAR- and AI-enhanced ROUV designed for near-real-time topographic mapping and navigation support in shallow, constrained waterways, demonstrated at three cliff sites in the Suez Canal. The system integrates three-dimensional Light Detection and Ranging (LiDAR) scanning, an Inertial Measurement Unit (IMU), and an onboard processor running the Robot Operating System (ROS) for Simultaneous Localization and Mapping (SLAM). To address data loss from underwater LiDAR (caused by scattering and reflection), we introduce an AI-driven optimisation module that reconstructs missing point cloud data and improves SLAM continuity. We also report propulsion and propeller design changes (propeller v05_1) that reduce flow turbulence and improve scan stability. We compare our approach to sonar-only ROUV mapping and to recent ROUV/LiDAR studies using metrics including point-cloud completeness, SLAM continuity, and navigation-path deviation. The main contributions are: (i) an integrated LiDAR, ROS and AI pipeline for underwater SLAM with missing-point recovery; (ii) a propulsion configuration optimized for LiDAR scanning stability; and (iii) a real-world Suez Canal case study demonstrating practical benefits for narrow-lane navigation.