Smart ROUV advances for enhanced navigation in the Suez Canal

Area of Science:

• Robotics and Autonomous Systems
• Geospatial Science and Technology
• Ocean Engineering

Background:

• Remotely Operated Underwater Vehicles (ROUVs) are crucial for high-resolution surveying of confined aquatic environments.
• Underwater LiDAR data acquisition faces challenges due to scattering and reflection, impacting mapping accuracy.
• Simultaneous Localization and Mapping (SLAM) is essential for ROUV navigation but can be degraded by data loss.

Purpose of the Study:

• To develop and demonstrate an AI-enhanced ROUV system (3Clifs) for near-real-time topographic mapping in shallow, constrained waterways.
• To improve the continuity and completeness of underwater LiDAR-based SLAM by addressing data loss.
• To enhance ROUV navigation support in narrow shipping lanes.

Main Methods:

• Integration of 3D LiDAR, Inertial Measurement Unit (IMU), and Robot Operating System (ROS) for underwater SLAM.
• Development of an AI-driven module for reconstructing missing LiDAR point cloud data.
• Optimization of ROUV propulsion and propeller design (propeller v05_1) to minimize flow turbulence and enhance scanning stability.

Main Results:

• The 3Clifs system demonstrated effective LiDAR and AI integration for underwater SLAM with missing-point recovery.
• Propulsion modifications significantly improved scan stability and reduced data gaps.
• Comparative analysis showed superior point-cloud completeness and SLAM continuity over sonar-only methods.

Conclusions:

• The integrated LiDAR, ROS, and AI pipeline offers a robust solution for underwater SLAM in challenging conditions.
• Optimized propulsion enhances the reliability of LiDAR-based mapping for ROUVs.
• The 3Clifs system provides practical benefits for navigation and mapping in narrow Suez Canal lanes.