Query: tunnel point cloud

title: Development of Inspection System for Tunnel Point Cloud

title: Tunnel Point Cloud and BIM Model Integration for Cross section Monitoring
abstract: This paper introduces a method for tunnel point cloud and BIM model integration and cross section monitoring, providing information to analyse tunnel cross sections and surrounding rock deformation, and support for tunnel maintenance and reconstruction. Three types of data are processed for the integration: laser scanning point cloud, BIM tunnel model and terrain model from oblique photogrammetry. An adaptive BIM modelling scheme is proposed for tunnels with alien structures. Precise spatial registration of the data sets is conducted by applying singular value decomposition (SVD) algorithm to calculate transformation parameters from the point cloud model to the BIM model. Since the tunnel central line has high order derivability, a crosssection calculation method based on tangent vector is proposed to obtain the cross sectional profile of tunnels at any mileage. The proposed method has been verified by applying it to a tunnel reconstruction project. The experiment results show that the tunnel point cloud and the BIM model were highly coincident after the integration. The developed program can effectively get the cross section of the tunnel at any mileage, and correctly express the spatial relationship between the BIM tunnel, the point cloud of tunnel and the external mountainous terrain.

title: TUNNEL POINT CLOUD FILTERING METHOD BASED ON ELLIPTIC CYLINDRICAL MODEL
abstract: The large number of bolts and screws that attached to the subway shield ring plates, along with the great amount of accessories of metal stents and electrical equipments mounted on the tunnel walls, make the laser point cloud data include lots of non tunnel section points (hereinafter referred to as non points) therefore affecting the accuracy for modeling and deformation monitoring. This paper proposed a filtering method for the point cloud based on the elliptic cylindrical model. The original laser point cloud data was firstly projected onto a horizontal plane, and a searching algorithm was given to extract the edging points of both sides, which were used further to fit the tunnel central axis. Along the axis the point cloud was segmented regionally, and then fitted as smooth elliptic cylindrical surface by means of iteration. This processing enabled the automatic filtering of those inner wall non points. Experiments of two groups showed coincident results, that the elliptic cylindrical model based method could effectively filter out the non points, and meet the accuracy requirements for subway deformation monitoring. The method provides a new mode for the periodic monitoring of tunnel sections all around deformation in subways routine operation and maintenance.

title: Study of Tunnel Surface Parameterization of 3 D Laser Point Cloud Based on Harmonic Map
abstract: In the maintenance work of tunnels, images are often used to detect diseases, but collections of tunnel images are limited by the tunnel environment and working time. Three dimensional laser scanning technology can acquire high precision tunnel information efficiently, and the main problem to be solved by using this technology to collect tunnel inner wall images is the dimensionality reduction of the laser tunnel point cloud data. This letter proposes a tunnel surface parameterization algorithm based on a harmonic map, where a 3 D tunnel point cloud is used as a data source to reconstruct a triangle mesh model of the tunnel and then generate a harmonic map depth map of the tunnel inner wall on the triangle mesh. We can obtain the spatial distribution and position information of the appendages and detect whether there are cracks, water leakage, falling pieces, and other diseases by the depth images. The results of this study indicate that the proposed algorithm is suitable for tunnels of various shapes and has low area distortion, which can better avoid the loss of information during dimensionality reduction. Compared with other existing methods, the algorithm has higher efficiency and applicability.

title: Tunnel Deformation Inspection via Global Spatial Axis Extraction from 3D Raw Point Cloud
abstract: Global inspection of large scale tunnels is a fundamental yet challenging task to ensure the structural stability of tunnels and driving safety. Advanced LiDAR scanners, which sample tunnels into 3D point clouds, are making their debut in the Tunnel Deformation Inspection (TDI) However, the acquired raw point clouds inevitably possess noticeable occlusions, missing areas, and noise/outliers. Considering the tunnel as a geometrical sweeping feature, we propose an effective tunnel deformation inspection algorithm by extracting the global spatial axis from the poor quality raw point cloud. Essentially, we convert tunnel axis extraction into an iterative fitting optimization problem. Specifically, given the scanned raw point cloud of a tunnel, the initial design axis is sampled to generate a series of normal planes within the corresponding Frenet frame, followed by intersecting those planes with the tunnel point cloud to yield a sequence of cross sections. By fitting cross sections with circles, the fitted circle centers are approximated with a B Spline curve, which is considered as an updated axis. The procedure of "circle fitting and B SPline approximation" repeats iteratively until convergency, that is, the distance of each fitted circle center to the current axis is smaller than a given threshold. By this means, the spatial axis of the tunnel can be accurately obtained. Subsequently, according to the practical mechanism of tunnel deformation, we design a segmentation approach to partition cross sections into meaningful pieces, based on which various inspection parameters can be automatically computed regarding to tunnel deformation. A variety of practical experiments have demonstrated the feasibility and effectiveness of our inspection method.

title: A method of tunnel critical rock identification and stability analysis based on a laser point cloud
abstract: The role of 3D laser scanning technology in the identification of critical rock in engineering is becoming increasingly important. In order to improve the recognition efficiency and accuracy of this identification, it is particularly significant to propose an analytical method for matching critical rock with point cloud technology. Based on the research foundation of 3D laser scanning technology in the field of rock mass structural surface identification, this paper proposes a critical rock identification and stability analysis method based on laser point cloud technology. First, a calculation method for a closed critical rock body is proposed. Second, block theory and point cloud technology are combined. Then, by utilizing the high density features of the point cloud, the vector analysis method of the traditional critical rock is converted into the space coordinates of the 3D point cloud, and the internal coordinate system of the 3D laser scanning system is directly used to extract the X, Y, and Z coordinate values of the corner points of the critical rock. Next, these coordinates are spatially compared and analyzed, and the key critical rock determination and its corresponding instability method are identified. Finally, the movable identification and stability analysis of the critical rock on the geometric scale is attained. This paper mainly summarizes the identification of closed critical rock bodies, the movable identification of key critical rock, and the determination methods of three types of critical rock instability modes as follows: falling type, single sided sliding type, and double sided sliding type. The method of the visual analysis process is implemented using C+ and OpenGL language programming. Eventually, the method is applied to practical engineering, and good results are obtained, which demonstrates the effectiveness of the method and makes it of great significance for future engineering applications.

title: Automatic Tunnel Steel Arches Extraction Algorithm Based on 3D LiDAR Point Cloud
abstract: Automation is an inevitable trend in the development of tunnel shotcrete machinery. Tunnel environmental perception based on 3D LiDAR point cloud has become a research hotspot. Current researches about the detection of tunnel point clouds focus on the completed tunnel with a smooth surface. However, few people have researched the automatic detection method for steel arches installed on a complex rock surface. This paper presents a novel algorithm to extract tunnel steel arches. Firstly, we propose a refined function for calibrating the tunnel axis by minimizing the density variance of the projected point cloud. Secondly, we segment the rock surface from the tunnel point cloud by using the region growing method with the parameters obtained by analyzing the tunnel section sequence. Finally, a Directed Edge Growing (DEG) method is proposed to detect steel arches on the rock surface in the tunnel. Our experiment in the highway tunnels under construction in Changsha (China) shows that the proposed algorithm can effectively extract the points of the edge of steel arches from 3D LiDAR point cloud of the tunnel without manual assistance. The results demonstrated that the proposed algorithm achieved 92.1% of precision, 89.1% of recall, and 90.6% of the F score.

title: Hierarchical tunnel modeling from 3D raw LiDAR point cloud
abstract: Precise modeling of tunnel structures can be used to evaluate the performance and state of safety as an important construction analysis object. However, the problem of modeling on a large scale tunnel is challenging, due to the poor quality of the input data that is contaminated with severe missing areas, noise, outliers and the accessories. This work introduces the concept of hierarchical modeling to automatically derive a measured and accurate representation of a 3D shield tunnel. Our key observation is that the shield tunnel is composed of a certain number of segment pieces arranged in some designed patterns. We hereby apply hierarchical segmentation and element extraction, based on designed patterns, to intensity images without expensive computation. According to the corresponding relationship between the point cloud and the image, the segment pieces within the point cloud are extracted. Therefore, we construct the corresponding 3D models of the segment pieces to obtain the precise structure model of the tunnel. As a result, segment pieces are extracted robustly, even in the presence of noise and large occlusion. Quantitative and qualitative comparisons of the proposed algorithm are presented with state of the art methods. We evaluate the modeling method on a variety of raw LiDAR scans, in terms of its robustness and accuracy. Moreover, our method has been successfully applied in a number of practical projects to discover the multi level structures of the shield tunnels, which are used to evaluate the safety state of tunnels.

title: Segmentation of Subway Tunnel Wall Surface Objects Based on Laser 3D Point Cloud

title: A Study on Adjustment Point for Detecting Sky Displacement in Tunnel Using Point Cloud Data