High-Precision Dynamic Point Cloud for Geohazard Monitoring Using Camera Array

Abstract Monitoring and preventing Geohazard such as landslides is a critical aspect of infrastructure engineering, essential for safeguarding infrastructure and communities. Effective slope stability analysis and failure monitoring demand the integration of comprehensive, field-acquired data to capture the complex dynamics of soil-structure interaction. While traditional methods such as remote sensing and aerial monitoring are commonly employed for geohazard assessment, they often fall short in providing the real-time capabilities necessary for timely intervention for risk assessment. Our approach leverages a network of multiple cameras to generate dynamic 3D point clouds, enabling continuous and detailed monitoring of soil stability and topographical changes. By producing scale-invariant 3D point cloud models for each frame, our method ensures the consistency and accuracy of geometry data, effectively eliminating physical scale variances that can occur during point cloud registration. This advanced system not only delivers accuracy on par with LiDAR but also provides the significant advantage of real-time analysis, making it possible to detect potential issues as they develop. By integrating this cutting-edge technology, we can significantly improve our capacity to forecast potential failure risks, allowing for more proactive management and mitigation of geohazards.