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Centerline-based registration for shield tunnel 3D reconstruction using spinning mid-range LiDAR point cloud and multi-cameras
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-10 , DOI: 10.1016/j.autcon.2024.105950
Liao Jian, Wenge Qiu, Yunjian Cheng

Mobile measurements can rapidly acquire tunnel information. However, cumulative errors in yaw angles occur in the absence or weakness of global positioning system (GPS) signals. This paper presents a method for 3D reconstruction of shield tunnels based on tunnel centerlines using non-repeating spinning mid-range LiDAR (SML) points and photos. First, a low-cost mobile measurement system (MMS) was built. Subsequently, the raw data were transformed into the tunnel centerline coordinate system (TCCS), including coarse registration with centerline alignment and fine registration based on convex hull areas. The multi-sensor data were fused in the TCCS, and photos were projected onto the SML points and unwrapped. Kilometrage corrections were applied by weighting the errors between the survey control points on panoramic images and their geodetic coordinates. Finally, the reconstructed data were located using image segmentation and indexing. This approach demonstrates higher registration accuracy in subway scenes than mainstream algorithms.

中文翻译:


使用旋转中距离 LiDAR 点云和多相机进行盾构隧道 3D 重建的基于中心线的配准



移动测量可以快速获取隧道信息。然而,在全球定位系统 (GPS) 信号缺失或较弱的情况下,会出现偏航角的累积误差。本文提出了一种基于隧道中心线的盾构隧道三维重建方法,使用非重复旋转中程 LiDAR (SML) 点和照片。首先,构建了一个低成本的移动测量系统 (MMS)。随后,将原始数据转换为隧道中心线坐标系 (TCCS),包括中心线对齐的粗配准和基于凸包面积的精细配准。在 TCCS 中融合多传感器数据,并将照片投影到 SML 点上并展开。通过对全景图像上的测量控制点与其大地坐标之间的误差进行加权来应用公里校正。最后,使用图像分割和索引定位重建的数据。该方法在地铁场景中的配准准确率高于主流算法。
更新日期:2025-01-10
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