A New Approach for Cylindrical Steel Structure Deformation Monitoring by Dense Point Clouds

As fundamental load-bearing parts, the cylindrical steel structures of transmission towers relate to the stability of the main structures in terms of topological relation and performance. Therefore, the periodic monitoring of a cylindrical steel structure is necessary to maintain the safety and stability of existing structures in energy transmission. Most studies on deformation analysis are still focused on the process of identifying discrepancies in the state of a structure by observing it at different times, yet relative deformation analysis based on the data acquired in single time has not been investigated effectively. In this study, the piecewise cylinder fitting method is presented to fit the point clouds collected at a single time to compute the relative inclination of a cylindrical steel structure. The standard deviation is adopted as a measure to evaluate the degree of structure deformation. Meanwhile, the inclination rate of each section is compared with the conventional method on the basis of the piecewise cylinder fitting parameters. The validity and accuracy of the algorithm are verified by real transmission tower point cloud data. Experimental results show that the piecewise cylinder fitting algorithm proposed in this research can meet the accuracy requirements of cylindrical steel structure deformation analysis and has high application value in the field of structure deformation monitoring.

3D Characterization of Sorghum Panicles Using a 3D Point Cloud Derived from UAV Imagery

Sorghum is one of the most important crops worldwide. An accurate and efficient high-throughput phenotyping method for individual sorghum panicles is needed for assessing genetic diversity, variety selection, and yield estimation. High-resolution imagery acquired using an unmanned aerial vehicle (UAV) provides a high-density 3D point cloud with color information. In this study, we developed a detecting and characterizing method for individual sorghum panicles using a 3D point cloud derived from UAV images. The RGB color ratio was used to filter non-panicle points out and select potential panicle points. Individual sorghum panicles were detected using the concept of tree identification. Panicle length and width were determined from potential panicle points. We proposed cylinder fitting and disk stacking to estimate individual panicle volumes, which are directly related to yield. The results showed that the correlation coefficient of the average panicle length and width between the UAV-based and ground measurements were 0.61 and 0.83, respectively. The UAV-derived panicle length and diameter were more highly correlated with the panicle weight than ground measurements. The cylinder fitting and disk stacking yielded R2 values of 0.77 and 0.67 with the actual panicle weight, respectively. The experimental results showed that the 3D point cloud derived from UAV imagery can provide reliable and consistent individual sorghum panicle parameters, which were highly correlated with ground measurements of panicle weight.

A Simplified Approach to Tolerance Verifications for Cylindrical Surfaces Using Point-Set Data

Cylindrical surface and its tolerance verification play important role in machining process. Although there exists many approaches that can fit the maximum, minimum and minimum zone cylinders, the cylinder fitting problems can even be further simplified. The methodology developed in this paper seeks to reduce the number of parameters used in cylinder fitting model using the projection model. The model evaluates 3-D tolerance specifications by introducing the optimal direction of projection such that the 2-D point projected onto this direction has optimal tolerance specifications (maximum, minimum and minimum zone circles). Besides, a global optimum solver, Particle Swarm Optimization (PSO) is applied to avoid the problem of finding local optimum. The proposed simplified method shows consistent results when compared with the results from literature.

ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA

This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD). Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete data, as street poles for example are only scanned from the road. Moreover, existence of outliers is common. Outliers may occur as random or systematic errors, and may be scattered and/or clustered. In this paper, we present a statistically robust cylinder fitting algorithm for PCD that combines Robust Principal Component Analysis (RPCA) with robust regression. Robust principal components as obtained by RPCA allow estimating cylinder directions more accurately, and an existing efficient circle fitting algorithm following robust regression principles, properly fit cylinder. We demonstrate the performance of the proposed method on artificial and real PCD. Results show that the proposed method provides more accurate and robust results: (i) in the presence of noise and high percentage of outliers, (ii) for incomplete as well as complete data, (iii) for small and large number of points, and (iv) for different sizes of radius. On 1000 simulated quarter cylinders of 1m radius with 10% outliers a PCA based method fit cylinders with a radius of on average 3.63 meter (m); the proposed method on the other hand fit cylinders of on average 1.02 m radius. The algorithm has potential in applications such as fitting cylindrical (e.g., light and traffic) poles, diameter at breast height estimation for trees, and building and bridge information modelling.