3D Reconstruction and Deformation Detection of Rescue Shaft Based on RGB-D Camera

Efficient and accurate 3D reconstruction of rescue shafts in mining accidents is a critical and challenging task, particularly in low-texture environments. This paper proposes a novel method for real-time 3D model reconstruction, deformation detection, and accessibility analysis of rescue shafts using an RGB-D camera. The approach captures depth and color data from the shaft's low-texture walls and employs advanced feature extraction and matching algorithms to generate a high-precision 3D point cloud. A hybrid Iterative Closest Point-Perspective n Point (ICP-PNP) algorithm ensures precise camera pose estimation, and motion errors between adjacent frames are minimized to optimize the 3D point cloud. The reconstructed model is refined using Poisson surface reconstruction, achieving millimeter-level pose estimation accuracy and a global trajectory consistency error within 2%. Experimental results demonstrate the superiority of the Speeded Up Robust Features (SURF) algorithm in feature extraction and the effectiveness of the Random Sample Consensus (RANSAC) algorithm in filtering mismatched points. The method also provides deformation profiles and accessibility predictions, with diameter estimates ranging from 510 mm to 540 mm, enabling accurate assessments of shaft usability and deformation trends. This framework enhances the precision and efficiency of rescue operations, offering a robust tool for real-time decision-making in mining emergencies.