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To address the inefficiencies and limited spatial resolution of traditional single-point monitoring techniques, this study proposes a multi-scale analysis method that integrates the Multi-Scale Model-to-Model Cloud Comparison (M3C2) algorithm with least-squares plane fitting. This approach employs the M3C2 algorithm for qualitative full-field deformation detection and utilizes least-squares plane fitting for quantitative feature extraction. When applied to the approach span of a cross-river bridge in Hubei Province, China, this method leverages dense point clouds (greater than 500 points per square meter) acquired using a Leica RTC360 scanner. Data preprocessing incorporates curvature-adaptive cascade denoising, achieving over 98% noise removal while retaining more than 95% of structural features, along with octree-based simplification. By extracting multi-level slice features from bridge decks and piers, this method enables the simultaneous analysis of global trends and local deformations. The results revealed significant deformation, with an average settlement of 8.2 mm in the left deck area. The bridge deck exhibited a deformation trend characterized by left and higher right in the vertical direction, while the bridge piers displayed noticeable tilting, particularly with the maximum offset of the rear pier columns reaching 182.2 mm, which exceeded the deformation of the front pier. The bridge deck’s micro-settlement error was ±1.2 mm, and the pier inclination error was ±2.8 mm, meeting the Chinese Highway Bridge Maintenance Code (JTG H11-2004) and the American Association of State Highway and Transportation Officials (AASHTO) standards, and the multi-scale algorithm achieved engineering-level accuracy. Utilizing point cloud densities >500 pt/m2, the M3C2 algorithm achieved a spatial resolution of 0.5 mm, enabling sub-millimeter full-field analysis for complex scenarios. This method significantly enhances bridge safety monitoring precision, enhances the precision of intelligent systems monitoring, and supports the development of targeted systems as pile foundation reinforcement efforts and as improvements to foundations.

Purpose>In order to improve the robustness to noise in point cloud plane fitting, a combined model of improved Cook’s distance (ICOOK) and WTLS is proposed by setting a modified Cook’s increment, which could help adaptively remove the noise points that exceeds the threshold.Design/methodology/approach>This paper proposes a robust point cloud plane fitting method based on ICOOK and WTLS to improve the robustness to noise in point cloud fitting. The ICOOK to denoise the initial point cloud was set and verified with experiments. In the meanwhile, weighted total least squares method (WTLS) was adopted to perform plane fitting on the denoised point cloud set to obtain the plane equation.Findings>(a) A threshold-adaptive Cook’s distance method is designed, which can automatically match a suitable threshold. (b) The ICOOK is fused with the WTLS method, and the simulation experiments and the actual fitting of the surface of the DD motor are carried out to verify the actual application. (c) The results shows that the plane fitting accuracy and unit weight variance of the algorithm in this paper are substantially enhanced.Originality/value>The existing point cloud plane fitting methods are not robust to noise, so a robust point cloud plane fitting method based on a combined model of ICOOK and WTLS is proposed. The existing point cloud plane fitting methods are not robust to noise, so a robust point cloud plane fitting method based on a combined model of ICOOK and WTLS is proposed.

We utilize a more accurate range noise model for 3D sensors to derive from scratch the expressions for the optimum plane fitting a set of noisy points and for the combined covariance matrix of the plane’s parameters, viz. its normal and its distance to the origin. The range error model used by us is a quadratic function of the true range and also the incidence angle. Closed-form expressions for the Cramér–Rao uncertainty bound are derived and utilized for analyzing four methods of covariance computation: exact maximum likelihood, renormalization, approximate least-squares, and eigenvector perturbation. The effect of the simplifying assumptions inherent in these methods are compared with respect to accuracy, speed, and ease of interpretation of terms. The approximate least-squares covariance matrix is shown to possess a number of desirable properties, e.g., the optimal solution forms its null-space and its components are functions of easily understood terms like the planar-patch’s weighted centroid and scatter. It is also fast to compute and accurate enough in practice. Its experimental application to real-time range-image registration and plane fusion is shown by using a commercially available 3D range sensor.

The Measurement-While-Drilling (MWD) system, composed of a tri-axial magnetometer and a tri-axial accelerometer, is widely used in the Horizontal Directional Drilling machine in coal mines. This system can provide attitude information of each measuring point in the borehole, which will eventually allow the trajectory of the borehole to be drawn. The attitude information, however, showed a low-level accuracy, due to the sensor’s imperfection and mounting errors. The accuracy worsened when low-cost sensors were employed, as they had higher random noise. Therefore, an exploration of ways to eliminate the sensor imperfection and mounting tolerance as well as to suppress the noise is needed. In this paper, a feasible calibration approach was designed to address these issues. This new approach combined three foundational calibration algorithms, including the ellipsoidal fitting method, the planar fitting method, and the inner product invariance method. The traditional ellipsoidal fitting method and planar fitting method were optimized by using the recursive least square criterion and omitting the steps of sample data acquisition, respectively. In addition, the noise suppression method was involved in our approach to improve the calibration accuracy. The numerical simulation results showed that the number of sampling points decreased significantly, but the accuracy of the azimuthal angle and the pitch angle fully met the engineering requirements. The experimental results showed that the pitch angle error was reduced by less than 0.5°, and the azimuth error was also reduced by less than 2.5°. It should be noted that this new approach could be implemented without the help of other expensive auxiliary equipment.

This paper presents a practical new approach for determining the geometric shape of an ancient pagoda structure and its axial shape along its height. Because of ancient Chinese pagodas’ unique characteristics (multiple eaves, Dougong, and vague edges), it is difficult to select appropriate measurement points. Moreover, the plane size of most ancient Chinese pagodas decreases from the bottom to the top floor. Considering these characteristics, we employed an electronic total station to monitor more than three measurement points of each wall façade of each pagoda floor, fitted plane equations using 3-D coordinates of the monitored measurement points and the least square method, calculated the corner points coordinates of each floor or cross section selected, fitted a circumscribed circle of each floor using the corner point coordinates and spatial axial equations based on the central coordinates, and performed an inclination evaluation of the ancient Chinese pagoda. This approach is practical and can be widely used in other high-rise polygonal buildings that have different plane sizes for each floor.

We propose an Image matching technique based on Cumulative Distribution Function, which provides a considerable reduction in the retrieval time. The two novel approaches called bit plane histogram and hierarchical bit plane histogram are discussed. Next, the image matching technique based on Cumulative Distribution Function is explained and a comparison of the various techniques is brought out. The CDF of the query and the images in the database are approximated by piecewise linear models with two parameters, slope and intercept at various grayscale intervals. The contiguous set of lines approximating the CDFs enables us to compare the query image and the images in the database with corresponding estimated slopes and intercepts. As the dynamic range of CDF is from 0 to 1, images of different sizes can be compared. Approximation of CDFs with lines further reduces the dimension of the image features and thus improves the speed of matching.

We present a camera calibration method based on circle plane board. The centres of circles on plane are regarded as the characteristic points, which are used to implement camera calibration. The proposed calibration is more accurate than many previous calibration algorithm because of the merit of the coordinate of circle centre being obtained from thousand of of edge pionts of ellipse, which is very reliable to image noise caused by edge extraction algorithm. Experiments shows the proposed algorithm can obtain high precise inner parameters, and lens distortion parameters.