Xianghong Hua

Initial Study on Information Quantity of Point Cloud

The measure method of information quantity for terrestrial laser scanner (TLS) 3D point cloud data have seldom been studied in previous publications. In this paper, we mainly discuss how to measure information quantity of point cloud data. We give the procedure of measuring information quantity for point cloud, and obtain the formula of calculation. Furthermore, we calculate the information quantity of 5 types of point cloud data, which verified the feasibility of the information measure theory for point cloud.

Evaluating point cloud accuracy of static three-dimensional laser scanning based on point cloud error ellipsoid model

Abstract. Evaluation of static three-dimensional (3-D) laser scanning point cloud accuracy has become a topical research issue. Point cloud accuracy is typically estimated by comparing terrestrial laser scanning data related to a finite number of check point coordinates against those obtained by an independent source of higher accuracy. These methods can only estimate the point accuracy but not the point cloud accuracy, which is influenced by the positional error and sampling interval. It is proposed that the point cloud error ellipsoid is favorable for inspecting the point cloud accuracy, which is determined by the individual point error ellipsoid volume. The kernel of this method is the computation of the point cloud error ellipsoid volume and the determination of the functional relationship between the error ellipsoid and accuracy. The proposed point cloud accuracy evaluation method is particularly suited for small sampling intervals when there exists an intersection of two error ellipsoids, and is suited not only for planar but also for nonplanar target surfaces. The performance of the proposed method (PM) is verified using both planar and nonplanar board point clouds. The results demonstrate that the proposed evaluation method significantly outperforms the existing methods when the target surface is nonplanar or there exists an intersection of two error ellipsoids. The PM therefore has the potential for improving the reliability of point cloud digital elevation models and static 3-D laser scanning-based deformation monitoring.

Performance Analysis for AP Selection Strategy

In recent years, received signal strength (RSS) based WiFi fingerprinting positioning technology has gradually become a research hotspot due to its ease of deployment and low cost implementation. However, the positioning accuracy of WiFi fingerprinting positioning based on RSS is affected by many factors. The quality of observed RSS is different among APs due to the complex and time varying indoor environment. Thus the selection of subset of optimal APs has a great influence on the RSS based WiFi fingerprinting positioning. This paper introduces three main AP selection algorithms: joint information gain (JIG) maximization based, mutual information (MI) minimization based and MaxMean (MM) based AP selection strategy, respectively. And the advantages and disadvantages of three different AP selection algorithms are compared and analyzed in this paper. At the same time, the influence of the number of subset of optimal APs and the number of RSS observations at the target point is comprehensively analyzed. Through the experiments, we found that: (1) for all the three algorithms, the positioning results tend to be stable when the number of real time RSS observations at the target point is more than 50; (2) with the given number of real time RSS observation at the target point, the position estimation accuracy change slowly with the increase of number of AP subset when the number of AP subset is more than 5; (3) given that the number of real-time RSS observations at the target point is 50 and the number of subset of optimal APs is 5, the position estimation accuracy of the AP selection strategy based on MI minimization is similar to the AP selection strategy based on MM, and both of them are better than the AP selection strategy based on the JIG maximization.

Extracting of six Deformation Parameters Using Improved ICP Matching Based on Terrestrial Laser Scanning Data

Extraction of the magnitude and direction of deformation is an important issue. Deformation is typically extracted by comparing data related to a finite number of control point. However, such a method can only extract the magnitude of deformation, but not the direction of deformation. In this paper, the improved ICP model is exploited for extracting the deformation. The main idea of this method is the construction of improved ICP and the determination of the relationship between extraction of six deformation parameters and local matching. This proposed deformation extraction method is particularly suited for scenarios where the deformation area is 3D rigid-body. The performance of the proposed method is extensively evaluated numerically and experimentally according to the 3D rigid-body board deformation. It is important to note that the conclusions were achieved under non-ideal conditions, e.g. using non-calibrated TLS point cloud and non-special targets. Besides the simulation experiment, the validation results achieved on bridge test site are briefly discussed.

Determining the Deformation Monitorable Indicator of Point Cloud Using Error Ellipsoid

As the development of the terrestrial laser scanning (TLS) technique, deformation monitoring using TLS has attracted increasing attention in the field. To distinguish the deformation from the error of TLS point cloud and evaluate the deformation monitorable capacity of the point cloud, a deformation monitorable indicator (DMI) should be determined. In this paper, a new method for determining the DMI of point cloud is proposed. The kernel procedure of the method is the establishment of point error ellipsoid and point cloud error ellipsoid and is described firstly in the paper. Then the computation of the actual point error ellipsoid is derived considering the intersection between the neighboring error ellipsoids. The determination of DMI comes in the next by calculating the point position error of the deformation orientation based on the actual point error ellipsoid. Furthermore, the performance of proposed approach is illustrated with validation experiments of planar board displacement where deformations with different sampling intervals, different scanning distances and different incidence angles were simulated. From the analysis of the experiments, the results show the validation of the feasibility of determining the DMI by the proposed method. This technique was also applied to a monitoring event of bridge pylon, and the results confirm the feasibility of the DMI in a real case, as well.

A New Progressive Simplification Method for Point Cloud Using Local Entropy of Normal Angle

Abstract With the development of modern 3D measurement technologies, it becomes easy to capture dense point cloud datasets. To settle the problem of pruning the redundant points and fast reconstruction, simplification for point cloud is a necessary step during the processing. In this paper, a new method is proposed to simplify point cloud data. The kernel procedure of the method is to evaluate the importance of points based on local entropy of normal angle. After the estimation of normal vectors, the importance evaluation of points is derived based on normal angles and the theory of information entropy. The simplification proceeds and finishes by removing the least important points and updating the normal vectors and importance values progressively until user-specified reduction ratio is reached. To evaluate the accuracy of the simplification results quantitatively, an indicator is determined by calculating the mean entropy of the simplified point cloud. Furthermore, the performance of the proposed approach is illustrated with two sets of validation experiments where other three classical simplification methods are employed for contrast. The results show that the proposed method performs much better than other three methods for point cloud simplification.

The Residuals Iteration Correction Algorithm of Exceptional Corresponding Points Based on Affine

The corresponding points are prerequisite to the registration of Terrestrial Laser Scanning data (TLS). The exceptional corresponding points will direct impact the quality of registration. The interest in this paper is in the so-called residuals iteration correction algorithm, which focused on a new procedure for correcting the exceptional corresponding points. The kernel of the procedure is the Affine proposed by Berger (1987). This paper describes the three main steps of residuals iteration correction algorithm based on Affine, namely the decomposition of exceptional corresponding points, the propagation of registration residuals, and the correction of exceptional corresponding points. The paper outlines the key advantages of the proposed approach, such as the capability to correct exceptional corresponding point automatically according to the point precision. Furthermore, it illustrates the performance of proposed approach with a validation experiment where two exceptional corresponding points were simulated and “3S” statue TLS data in Wuhan University was acquired. From the analysis of this experiment, the result shows that the validation of correction of exceptional corresponding points based on residuals iteration.