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Objective. To examine the shape change of screw-rod constructs over time after short-segment lumbar interbody fusion and to clarify its relationship to clinical characteristics. Summary of Background Data. No study has focused on the shape change of screw-rod constructs after short-segment fusion and its clinical implications. Methods. One hundred eight patients who had single-level lumbar interbody fusion with pedicle screws and cages were enrolled. Three-dimensional (3D) images of screw-rod constructs were generated from baseline CT on the day after surgery and followup CT and were superposed on the right and left side, respectively, using the iterative closest point algorithm. The shape change was quantitatively assessed by computing the median distance between the 3D images, which was defined as the shape change value. Among the 5 time-course categories of follow-up CT (=1, 2-3, 4-6, 7-12, and =13 months), the shape change values were compared. The relationships between the shape change values and clinical characteristics, such as age, CT-derived vertebral bone mineral density, screw and rod materials, and postoperative interbody fusion status, cage subsidence, and screw loosening, were evaluated. Results. A total of 237 follow-up CTs were included (=1 [34 scans], 2-3 [33 scans], 4-6 [80 scans], 7-12 [48 scans], and = 13 months [42 scans]) because many patients underwent multiple follow-up CTs. There were significant differences in shape change values among the time-course categories (P<0.001 in Kruskal-Wallis test). Most shape changes occurred within 6 months postoperatively, with no significant changes observed at 7 months or more. There were no significant relationships between the shape change values and each clinical characteristic. Conclusions. The temporal shape changes of screw-rod constructs following short-segment lumbar interbody fusion progressed up to 6 months after surgery but not significantly thereafter. [ABSTRACT FROM AUTHOR]

The traditional Iterative Closest Point (ICP) algorithm often suffers from low computational accuracy and efficiency in certain scenarios. It is highly sensitive to the initial pose, has a poor ability to resist interference, and frequently becomes trapped in local optima. Extracting feature points accurately from partially overlapping points with weak three-dimensional features, such as smooth planes or surfaces with low curvature, is challenging using only the traditional ICP algorithm for registration. This research introduces a "First Rough then Precise" registration strategy. Initially, the target position is extracted in complex environments using an improved clustering method, which simultaneously reduces the impact of environmental factors and noise on registration accuracy. Subsequently, an improved method for calculating normal vectors is applied to the Fast Point Feature Histogram (FPFH) to extract feature points, providing data for the Sample Consistency Initial Algorithm (SAC-IA). Lastly, an improved ICP algorithm, which has strong anti-interference capabilities for partially overlapping point clouds, is utilized to merge such point clouds. In the experimental section, we validate the feasibility and precision of the proposed algorithm by comparing its registration outcomes with those of various algorithms, using both standard point cloud dataset models and actual point clouds obtained from camera captures. [ABSTRACT FROM AUTHOR]

Point cloud registration is a fundamental problem in computer vision. The problem encompasses critical tasks such as feature estimation, correspondence matching, and transformation estimation. The point cloud registration problem can be cast as a quantile matching problem. We refined the quantile assignment algorithm by integrating prevalent feature descriptors and transformation estimation methods to enhance the correspondence between the source and target point clouds. We evaluated the performances of these descriptors and methods with our approach through controlled experiments on a dataset we constructed using well-known 3D models. This systematic investigation led us to identify the most suitable methods for complementing our approach. Subsequently, we devised a new end-to-end, coarse-to-fine pairwise point cloud registration framework. Finally, we tested our framework on indoor and outdoor benchmark datasets and compared our results with state-of-the-art point cloud registration methods. [ABSTRACT FROM AUTHOR]

In this paper, a point cloud coarse–fine registration method based on a new improved version of the whale optimization algorithm (NIWOA) and iterative closest point (ICP) algorithm is proposed; we use three strategies to improve the whale optimization algorithm (WOA). Firstly, circle chaotic mapping is used to initialize the whale population to increase the diversity of the population and make the search space more comprehensively explored. In addition, a Newton inertia weight is proposed to flexibly adjust the proportion of global exploration and local optimization in order to achieve the balance between the exploitation performance and exploration ability of the algorithm. At the same time, we introduce the nonlinear convergence factor that can adjust the size adaptively so that the algorithm can find the global optimal solution faster and more accurately, allowing it to avoid falling into the local optimal solution to a certain extent. The NIWOA algorithm is used to optimize the objective function of point cloud coarse registration to obtain the optimal coordinate transformation, and the rotation and translation operation is carried out on the registered point cloud. The obtained position is used as the initial pose of the ICP fine registration, and the final registration is achieved through ICP iteration. We conduct coarse registration experiments on multiple model point clouds and scene point clouds using the Stanford 3D Scanning Repository dataset and Princeton 3Dmatch dataset, respectively. The experimental results confirm that the NIWOA algorithm can not only find the initial position that is closer to the target point cloud, but also provide reliable initial values for the ICP algorithm. Meanwhile, the NIWOA algorithm combined with ICP experiment results show that the method has a higher registration accuracy and operation efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a comprehensive error compensation method for the glass edging machine using polar coordinates. An error model is established to evaluate the error source of the contour deviation from the ideal position after glass installation. According to the simulation results, it is determined that the misalignment of the linear axis and the rotary table and the inaccuracy of the fixture are the main reasons for the installation error of the work-piece. Then, a two-step contour registration method is introduced to accomplish the task of error identification. The glass contour is first measured by a laser displacement sensor after being installed on the rotary table. The coarse registration is presented to align the centroid positions of the measured and the ideal contours, and the fine registration is developed to match them precisely. After that, the installation error can be extracted from the transformation parameters by the least square method. Furthermore, a compensation model is proposed to map the original contour to the actual installation position using the transformation matrix constructed by the identified errors. Finally, a verification experiment is implemented that compares the grind edge without and with compensation to validate the efficiency and accuracy of the proposed method. After error compensation, the mean value and the variance value of the polar diameter deviation of the glass work-piece decreased by 87.5% and 83.0%, respectively. This paper provides a simple way to identify and compensate the installation errors online with high accuracy and efficiency. The proposed method can improve the machining accuracy of the polar coordinate glass edging machines and reduce glass waste. • An error compensation method for glass edging machines was presented. • A two-step contour registration method is developed to accomplish error identification. • The installation errors was identified and compensated online with high accuracy. • The proposed algorithm can save at least 6% of the glass material. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) point cloud registration is a fundamental step for many 3D modeling and mapping applications. Existing approaches are highly disparate in the data source, scene complexity, and application, therefore the current practices in various point cloud registration tasks are still ad-hoc processes. Recent advances in computer vision and deep learning have shown promising performance in estimating rigid/similarity transformation between unregistered point clouds of complex objects and scenes. However, their performances are mostly evaluated using a limited number of datasets from a single sensor (e.g. Kinect or RealSense cameras), lacking a comprehensive overview of their applicability in photogrammetric 3D mapping scenarios. In this work, we provide a comprehensive review of the state-of-the-art (SOTA) point cloud registration methods, where we analyze and evaluate these methods using a diverse set of point cloud data from indoor to satellite sources. The quantitative analysis allows for exploring the strengths, applicability, challenges, and future trends of these methods. In contrast to existing analysis works that introduce point cloud registration as a holistic process, our experimental analysis is based on its inherent two-step process to better comprehend these approaches including feature/keypoint-based initial coarse registration and dense fine registration through cloud-to-cloud (C2C) optimization. More than ten methods, including classic hand-crafted, deep-learning-based feature correspondence, and robust C2C methods were tested. We observed that the success rate of most of the algorithms are fewer than 40% over the datasets we tested and there are still are large margin of improvement upon existing algorithms concerning 3D sparse corresopondence search, and the ability to register point clouds with complex geometry and occlusions. With the evaluated statistics on three datasets, we conclude the best-performing methods for each step and provide our recommendations, and outlook future efforts.

Computer-aided surgical planning has been widely used to increase the safety and predictability of surgery. The validation of the target of surgical planning to surgical outcomes on a patient-specific model is an important issue. The aim of this research was to develop a robust superposition method to assess the deviation of planning and outcome by using the symmetrical characteristic of the affected target. The optimal symmetry plane (OSP) of an object is usually used to evaluate the degree of symmetry of an object. We proposed a refined OSP-based contouring method to transfer a complex three-dimensional superposition operation into two dimensions. We compared the typical iterative closest point (ICP) algorithm with the refined OSP-based contouring method and examined the differences between them. The results using the OSP-based method were much better than the traditional method. As for processing time, the OSP-based contouring method was 11 times faster than the ICP method overall. The proposed method was not affected by the metallic artifacts from medical imaging or geometric changes due to surgical intervention. This technique can be applied for post-operative assessment, such as quantifying the differences between surgical targets and outcomes as well as performing long-term medical follow-up. [ABSTRACT FROM AUTHOR]

In this paper, a point cloud coarse–fine registration method based on a new improved version of the whale optimization algorithm (NIWOA) and iterative closest point (ICP) algorithm is proposed; we use three strategies to improve the whale optimization algorithm (WOA). Firstly, circle chaotic mapping is used to initialize the whale population to increase the diversity of the population and make the search space more comprehensively explored. In addition, a Newton inertia weight is proposed to flexibly adjust the proportion of global exploration and local optimization in order to achieve the balance between the exploitation performance and exploration ability of the algorithm. At the same time, we introduce the nonlinear convergence factor that can adjust the size adaptively so that the algorithm can find the global optimal solution faster and more accurately, allowing it to avoid falling into the local optimal solution to a certain extent. The NIWOA algorithm is used to optimize the objective function of point cloud coarse registration to obtain the optimal coordinate transformation, and the rotation and translation operation is carried out on the registered point cloud. The obtained position is used as the initial pose of the ICP fine registration, and the final registration is achieved through ICP iteration. We conduct coarse registration experiments on multiple model point clouds and scene point clouds using the Stanford 3D Scanning Repository dataset and Princeton 3Dmatch dataset, respectively. The experimental results confirm that the NIWOA algorithm can not only find the initial position that is closer to the target point cloud, but also provide reliable initial values for the ICP algorithm. Meanwhile, the NIWOA algorithm combined with ICP experiment results show that the method has a higher registration accuracy and operation efficiency.

BACKGROUND: Computer-aided design and three-dimensional printing have been used in various clinical and fundamental medicine fields, especially in surgery. For example, in the preoperative period, the correspondence of printed products to the anatomy can play an important role in evaluating pathological changes and correction methods. However, determining dimensional deviations of printed models involves ethical and technical difficulties associated with defining a reference and taking many measurements, respectively. Therefore, we propose to use a geometric object with known dimensions as a reference and estimate linear deviations using the Iterative Closest Point algorithm for each of the vertices of the prototyped polygonal mesh. AIMS: To evaluate the geometric deviations associated with creation of bone-like physical objects from computed tomography data using computer-aided design and additive manufacturing. MATERIALS AND METHODS: The source object was created using the FreeCAD application; Blender and Meshmixer software was used for polygon meshes correction and transformation. The 3D printing was carried out on an Ender-3 printer with copper-impregnated polylactide plastic BFCopper. Scanning was performed using a 128-slice tomograph Philips Ingenuity CT. A series of tomographic images were processed in 3DSlicer software to create virtual models by semiautomatic segmentation with threshold values of 500 HU, 0 HU, 500 HU, 750 HU, and manual segmentation. Reproduced and reference polygon meshes were compared using the Iterative Closest Point algorithm in CloudCompare software. RESULTS: The volume of reproduced models exceeded the volume of respective reference models by 1%27%. The average point cloud linear deviation values of reproduced models from the reference ones were 0.030.41 mm. A significant correlation between integral sums of linear deviations and changes in the volume of reproduced models was shown using Spearman's rank correlation coefficient ( = 0.83; temp = 5.27, p=0.05). CONCLUSION: The geometry of the reproduced object changes inevitably, while the linear deviations depend more on the chosen segmentation method than on the overall size of the model or its structures. The manual segmentation method can lead to greater linear deviations, though it saves all the necessary anatomical structures.

The iterative closest point (ICP) algorithm and its variants are a fundamental technique for rigid registration between two point sets, with wide applications in different areas from robotics to 3D reconstruction. The main drawbacks for ICP are its slow convergence, as well as its sensitivity to outliers, missing data, and partial overlaps. Recent work such as Sparse ICP achieves robustness via sparsity optimization at the cost of computational speed. In this paper, we propose a new method for robust registration with fast convergence. First, we show that the classical point-to-point ICP can be treated as a majorization-minimization (MM) algorithm, and propose an Anderson acceleration approach to speed up its convergence. In addition, we introduce a robust error metric based on the Welsch’s function, which is minimized efficiently using the MM algorithm with Anderson acceleration. On challenging datasets with noises and partial overlaps, we achieve similar or better accuracy than Sparse ICP while being at least an order of magnitude faster. Finally, we extend the robust formulation to point-to-plane ICP, and solve the resulting problem using a similar Anderson-accelerated MM strategy. Our robust ICP methods improve the registration accuracy on benchmark datasets while being competitive in computational time. [ABSTRACT FROM AUTHOR]

The k-Nearest Neighbors algorithm is a fundamental algorithm that finds applications in many fields like Machine Learning, Computer Graphics, Computer Vision, and others. The algorithm determines the closest points (d-dimensional) of a reference set R according to a query set of points Q under a specific metric (Euclidean, Mahalanobis, Manhattan, etc.). This work focuses on the utilization of multiple Graphical Processing Units for the acceleration of the k-Nearest Neighbors algorithm with large or very large sets of 3D points. With the proposed approach the space of the reference set is divided into a 3D grid which is used to facilitate the search for the nearest neighbors. The search in the grid is performed in a multiresolution manner starting from a high-resolution grid and ending up in a coarse one, thus accounting for point clouds that may have non-uniform sampling and/or outliers. Three important algorithms in reverse engineering are revisited and new multi-GPU versions are proposed based on the introduced KNN algorithm. More specifically, the new multi-GPU approach is applied to the Iterative Closest Point algorithm, to the point cloud smoothing, and to the point cloud normal vectors computation and orientation problem. A series of tests and experiments have been conducted and discussed in the paper showing the merits of the proposed multi-GPU approach. [ABSTRACT FROM AUTHOR]

Typically, airborne laserscanning includes a laser mounted on an airplane or drone (its pulsed beam direction can scan in flight direction and perpendicular to it) an intertial positioning system of gyroscopes, and a global navigation satellite system. The data, relative orientation and relative distance of these three systems are combined in computing strips of ground surface point locations in an earth fixed coordinate system. Finally, all laserscanning strips are combined via iterative closest point methods to an interactive three-dimensional terrain map. In this work we describe the mathematical framework for how to use the iterative closest point method for the adjustment of the airborne laserscanning data strips in the framework of conformal geometric algebra. [ABSTRACT FROM AUTHOR]

Normally, small batch parts, especially by manual assembly, are difficult to be manufactured automatically(e.g., welded, polished, sprayed) by robots because of deviation from a pre-taught path, which results from shape error and clamping error. To correct the pre-taught path, visual sensors can be used to detect it and guide the robots. However, some of the paths cannot be continuously detected due to work space limitation, and therefore, only part of them can be detected. As a result, it is difficult to directly match the incompleted detected path to pre-taught path with location errors and shape errors, which cause residual deviation after correction. This paper proposes an efficient and robust path correction method, that can be applied to robotic autonomous systems. The main contribution of this work is adopting global matching to maintain the original shape of the pre-taught path, which deals with location errors, and the local matching relaxes this constraint and addressed the shape errors. Structured light vision is used to detect the deviation of path to improve the detecting accuracy and reduce detection data size simultaneously. And a path-matching method taking account of both global and local features is developed based on iterative closest point algorithm (ICP). The proposed method consists of four steps: path scanning, global matching, local matching and data updating. Several robotic welding experiments are applied to verify the proposed method performance. [ABSTRACT FROM AUTHOR]

The most popular algorithm for registering clouds of points in 3D space is the iterative closest point (ICP) algorithm. The point-to-point variational problem for orthogonal transformations is mathematically equivalent to the absolute orientation problem in photogrammetry. In this paper, we briefly overview closed-form solutions to the point-to-point variational problem. The well-known Horn algorithm solves the problem for the O(3) group. We propose a modified Horn algorithm that makes it possible to solve the problem for the SO(3) group. Computer simulation illustrates the registration accuracy of the considered methods for various geometric transformations. [ABSTRACT FROM AUTHOR]

Computer-aided surgical planning has been widely used to increase the safety and predictability of surgery. The validation of the target of surgical planning to surgical outcomes on a patient-specific model is an important issue. The aim of this research was to develop a robust superposition method to assess the deviation of planning and outcome by using the symmetrical characteristic of the affected target. The optimal symmetry plane (OSP) of an object is usually used to evaluate the degree of symmetry of an object. We proposed a refined OSP-based contouring method to transfer a complex three-dimensional superposition operation into two dimensions. We compared the typical iterative closest point (ICP) algorithm with the refined OSP-based contouring method and examined the differences between them. The results using the OSP-based method were much better than the traditional method. As for processing time, the OSP-based contouring method was 11 times faster than the ICP method overall. The proposed method was not affected by the metallic artifacts from medical imaging or geometric changes due to surgical intervention. This technique can be applied for post-operative assessment, such as quantifying the differences between surgical targets and outcomes as well as performing long-term medical follow-up.

This study presents a new point set registration method to align 3D range scans. In our method, fuzzy clusters are utilized to represent a scan, and the registration of two given scans is realized by minimizing a fuzzy weighted sum of the distances between their fuzzy cluster centers. This fuzzy cluster-based metric has a broad basin of convergence and is robust to noise. Moreover, this metric provides analytic gradients, allowing standard gradient-based algorithms to be applied for optimization. Based on this metric, the outlier issues are addressed. In addition, for the first time in rigid point set registration, a registration quality assessment in the absence of ground truth is provided. Furthermore, given specified rotation and translation spaces, we derive the upper and lower bounds of the fuzzy cluster-based metric and develop a branch-and-bound (BnB)-based optimization scheme, which can globally minimize the metric regardless of the initialization. This optimization scheme is performed in an efficient coarse-to-fine fashion: First, fuzzy clustering is applied to describe each of the two given scans by a small number of fuzzy clusters. Then, a global search, which integrates BnB and gradient-based algorithms, is implemented to achieve a coarse alignment for the two scans. During the global search, the registration quality assessment offers a beneficial stop criterion to detect whether a good result is obtained. Afterwards, a relatively large number of points of the two scans are directly taken as the fuzzy cluster centers, and then, the coarse solution is refined to be an exact alignment using the gradient-based local convergence. Compared to existing counterparts, this optimization scheme makes a large improvement in terms of robustness and efficiency by virtue of the fuzzy cluster-based metric and the registration quality assessment. In the experiments, the registration results of several 3D range scan pairs demonstrate the accuracy and effectiveness of the proposed method, as well as its superiority to state-of-the-art registration approaches. [ABSTRACT FROM AUTHOR]

Iterative closest point algorithms suffer from non-convergence and local minima when dealing with cloud points with a different sampling density. Alternative global or semi-global registration algorithms may suffer from efficiency problem. This paper proposes a new registration algorithm through the differential topological singularity points (DTSP) based on the Helmholtz-Hodge decomposition (HHD), which is called DTSP-ICP method. The DTSP-ICP method contains two algorithms. First, the curvature gradient fields on surfaces are decomposed by the HHD into three orthogonal parts: divergence-free vector field, curl-free vector field, and a harmonic vector field, and then the DTSP algorithm is used to extract the differential topological singularity points in the curl-free vector field. Second, the ICP algorithm is utilized to register the singularity points into one aligned model. The singularity points represent the feature of the whole model, and the DTSP algorithm is designed to capture the nature of the differential topological structure of a mesh model. Through the singularity alignment, the DTSP-ICP method, therefore, possesses better performance in triangular model registration. The experimental results show that independent of sampling schemes, the proposed DTSP-ICP method can maintain convergence and robustness in cases where other alignment algorithms including the ICP alone are unstable. Moreover, this DTSP-ICP method can avoid the local errors of model registration based on Euclidean distance and overcome the computation insufficiencies observed in other global or semi-global registration publications. Finally, we demonstrate the significance of the DTSP-ICP algorithm's advantages on a variety of challenging models through result comparison with that of two other typical methods.

Internet and other online media networks have emerged as the most important platforms for the sharing of digital information. However, the readily available editing tools provide an easy way for adversaries to manipulate the data and affect decision-making in various industrial applications. This malicious modification of the content, which has reduced the credibility of information delivery, is a commonly prevalent issue and hence needs serious attention. It also initiates an extreme need for industrial cyber–physical systems (ICPS), which can compare the transferred and received images for correct orientation to ensure that it conveys meaningful information and assists in correct decision-making in industrial automation. In this article, we propose “INDFORG”, which employs a novel and highly accurate automatic rotation angle detection and correction algorithm (ARADC) for intelligent detection of forgery in industrial images. ARADC uses basic geometrical concepts, such as Pythagorean theorem and intensity correlation computation and works without any digital signature or watermark. It performs accurately even under several simultaneous signal-processing manipulations. The proposed framework detects the rotation angles blindly with a 99% accuracy rate for rotation up to ±89°. Experimental results prove that the proposed algorithm is highly efficient compared to various state-of-the-art approaches and is a preferred ICPS for trustworthy media delivery in industrial automation. [ABSTRACT FROM AUTHOR]

Langmuir probe (LP) diagnostics is performed on an inductively coupled plasma (ICP) generated using a flat spiral antenna. The current–voltage (I - V) characteristics are measured using a radio frequency (RF) compensated LP to mitigate the effect of RF distortion in I - V measurements. All-important plasma parameters such as electron density (ne), electron temperature (Te), plasma potential (Vp), and electron energy distribution function (EEDF) are measured in a cylindrical-shaped ICP. The plasma is generated by feeding RF (13.56 MHz) current in the planer multiple spiral antennas. It is seen that the plasma is uniform over an area with 200 mm diameter, with an electron number density in the range from ~ 1011 to 1012 cm−3. Typically for plasma processing of materials, the required density fall in that region. Variation of plasma parameters is investigated as a function of applied RF power and operating pressure and the observed results are discussed. [ABSTRACT FROM AUTHOR]

This article is focused on a challenging topic emerging from the registration of point clouds, specifically the registration of dynamic objects with low overlapping ratio. This problem is especially difficult when the static scanner is installed on a floating platform, and the objects it scans are also floating. These issues make most of the automatic registration methods and software solutions invalid. To solve this problem, explicit exploration of the static region is necessary for both the coarse and fine registration steps. Fortunately, determining the corresponding regions can be eased by the intuitive realization that in urban environments, natural objects neither present straight boundaries nor stack vertically. This intuition has guided the authors to develop a robust approach for the detection of static regions using planar structures. Then, silhouettes of the objects are extracted from the planar structures, which assist in the determination of an SE(2) transformation in the horizontal direction by a novel line matching method. The silhouettes also enable identification of the correspondences of planes in the step of fine registration using a variant of the iterative closest point method. Experimental evaluations using point clouds of cargo ships with different sizes and shapes reveal the robustness and efficiency of the proposed method, which gives 100% success and reasonable accuracy in rapid time, suitable for an online system. In addition, the proposed method is evaluated systematically with regard to several practical situations caused by the floating platform, and it demonstrates good robustness to limited scanning time and noise. [ABSTRACT FROM AUTHOR]

Nowadays, network users' demands for low latency services and individual data security grow rapidly. In this article, we conceive a new communication network architecture based on the concept of intelligent agents. The proposed intelligent agent serves as a virtual secretary of the corresponding user to request desired service from Internet companies or neighboring agents, which can reduce Internet surfing latency and prevent privacy leakage. Moreover, to improve the forwarding efficiency of data packages, a new mixed routing structure and the AI router are also introduced, to support various on-demand services with low latency. Overall, the proposed communication network architecture is expected to be more efficient than the existing one, and inspire readers to deliberate the revolution of future communications networks. [ABSTRACT FROM AUTHOR]

The 3-D registration is extensively required in many industrial applications involving 3-D perception and reconstruction. However, conventional methods consisting of singular value decomposition (SVD) or eigendecomposition are all hard to be implemented and are difficult to be ported using simple digital circuit prototypes. A novel solution is obtained to solve the rigid 3-D registration problem, motivated by previous eigendecomposition approaches. Different from existing solvers, the proposed algorithm does not require sophisticated matrix operations, e.g., SVD or eigenvalue decomposition. Instead, the optimal eigenvector of the point cross-covariance matrix can be computed within several iterations. It is also proven that the optimal rotation matrix can be directly computed for cases without the need for a quaternion. The simple framework provides a very easy approach, even without floating-number processing. Simulations on noise-corrupted point clouds have verified the robustness and computation speed of the proposed method. The final results indicate that the proposed algorithm is accurate, robust, and owns over 60%–80% less computation time than representatives. It has also been applied to real-world applications for faster robotic visual perception. [ABSTRACT FROM AUTHOR]

This study addresses the problem of non‐perspective pose estimation from line correspondences in the traffic scenarios. A coarse‐to‐fine 3D road registration method is proposed for this problem in two stages. Firstly, the iterative closest point algorithm is exploited to estimate the pose coarsely. An objective function is then established to incorporate the feature correspondences for refining the coarse pose. Besides, the framework including road registration is employed for traffic video augmentation. The framework begins with the inputs of traffic videos, road information from Geographic Information Systems and 3D models of traffic elements (e.g. vehicles, pedestrians). Subsequently, 3D road model generation and point‐to‐line correspondence establishment are achieved in the preprossessing stage. After road and viewpoint registration, the 3D graphic engine is employed to simulate the traffic scene with the road, viewpoints and traffic elements. The augmented videos are generated by fusing the original frames and newly projected traffic elements. The authors demonstrate the superiority of the proposed registration method by the comparison to state‐of‐the‐arts in both quantitative and qualitative experiments. In addition, the frames of the augmented videos validate the proposed method in the application. [ABSTRACT FROM AUTHOR]

Multi-view registration plays a critical role in 3D model reconstruction. To solve this problem, most previous methods align point sets by either partially exploring available information or blindly utilizing unnecessary information, which may lead to undesired results or extra computation complexity. Accordingly, we propose a novel solution for the multi-view registration under the perspective of Expectation-Maximization (EM). The proposed method assumes that each data point is generated from one unique Gaussian Mixture Model (GMM), where its corresponding points in other point sets are regarded as Gaussian centroids with equal covariance and membership probabilities. As it is difficult to obtain real corresponding points in the registration problem, they are approximated by the nearest neighbor in each other aligned point sets. Based on this assumption, it is reasonable to define the likelihood function including all rigid transformations, which require to be estimated for multi-view registration. Subsequently, the EM algorithm is derived to estimate rigid transformations with one Gaussian covariance by maximizing the likelihood function. Since the GMM component number is automatically determined by the number of point sets, there is no trade-off between registration accuracy and efficiency in the proposed method. Finally, the proposed method is tested on several benchmark data sets and compared with state-of-the-art algorithms. Experimental results demonstrate its superior performance on the accuracy, efficiency, and robustness for multi-view registration. [ABSTRACT FROM AUTHOR]

Handling deformation is one of the biggest challenges associated with point cloud registration. When deformation happens due to the motion of an animated object which actively changes its location and general shape, registration of two instances of the same object turns out to be a challenging task. The focus of this work is to address the problem by leveraging the complementary attributes of local and global geometric structures of the point clouds. We define an energy function which consists of local and global terms, as well as a semi-local term to model the intermediate level geometry of the point cloud. The local energy estimates the transformation parameters at the lowest level by assuming a reduced deformation model. The parameters are estimated in a closed form solution, which are then used to assign the initial probability of a stochastic model working at the intermediate level. The global energy term estimates the overall transformation parameters by minimizing a nonlinear least square function via Gauss-Newton optimization framework. The total energy is optimized in a block coordinate descent fashion, updating one term at a time while keeping others constant. Experiments on three publicly available datasets show that the method performs significantly better than several state-of-the-art algorithms in registering pairwise point cloud data. [ABSTRACT FROM AUTHOR]

Point set registration is a problem with a long history in many pattern recognition tasks. This paper presents a robust point set registration algorithm based on optimizing the distance between two probability distributions. A major problem in point to point algorithms is defining the correspondence between two point sets. This paper follows the idea of some probability-based point set registration methods by representing the point sets as Gaussian mixture models (GMMs). By optimizing the distance between the two GMMs, rigid transformations (rotation and translation) between two point sets can be obtained without having to find a correspondence. Previous studies have used L2, Kullback Leibler, etc. distance to measure similarity between two GMMs; however, these methods have problems with robustness to noise and outliers, especially when the covariance matrix is large, or a local minimum exists. Therefore, in this paper, the signature quadratic form distance is derived to measure the distribution similarity. The contribution of this paper lies in adopting the signature quadratic form distance for the point set registration algorithm. The experimental results show the precision and robustness of this algorithm and demonstrate that it outperforms other state-of-the-art point set registration algorithms regarding factors, such as noise, outliers, missing partial structures, and initial misalignment. [ABSTRACT FROM AUTHOR]

Free-form surface matching that aligns measured points with a design model is a common problem in manufacturing automation. In this paper, an iterative variance-minimization matching (VMM) method is proposed to address measured points that have measuring defects, such as uneven/open point distributions and measuring noise. The basic idea is that the objective function is defined as the variance of the closest distance from each measured point to the design model, and the measuring defects are considered by incorporating an average distance item into the objective function. Using the defined average distance item, a strategy for analyzing the effect of measuring defects on VMM and existing methods is presented. It is shown that the VMM method does not easily become trapped in a local optimum when measuring defects exist. To consider convergence speed and convergence stability, a new distance based on the first-order point-to-point distance and point-to-tangent distance is developed and used in the objective function. To demonstrate the availability of the proposed method, quadratic convergence and positive definiteness are theoretically analyzed. The proposed method is efficient and insensitive to measuring defects and is useful for shape matching tasks involving free-form surface features. Note to Practitioners—This paper is motivated by the problem of matching measured points with a design model to automate manufacturing processes such as geometric inspection, workpiece localization, and allowance distribution. Measured points are obtained by applying a scanning device where measuring defects usually appear. Existing matching methods suffer from the drawback that the measured points may incline toward dense data and become trapped in a local optimum, due to measuring defects. To address this practical issue, this paper proposes a new method called variance-minimization matching (VMM), in which the objective function is optimized to weaken the effect of measuring defects. By examining the differences between VMM and existing methods, it is found that VMM can achieve quadratic convergence speed. Most importantly, the method is insensitive to uneven/open point distributions. In summary: 1) this method allows us to improve the matching accuracy in the presence of measuring defects; 2) there is no need to obtain a high-quality scan of the entire workpiece, potentially reducing scanning difficulty and improving scanning efficiency; and 3) the requirement of uniform sampling for measured points is reduced. [ABSTRACT FROM AUTHOR]

In the first part of this paper, a free-form surface matching method called variance-minimization matching (VMM) was proposed to address uneven/open point distributions and measuring noise. The convergence property and sensitivity to measuring defects were theoretically studied. In the second part of this paper, a series of experiments are presented to verify the feasibility of the proposed method in free-form surface matching. The experiments are divided into four sets: a measuring defects experiment, a noise experiment, a convergence experiment, and an artificial experiment. In the first set of experiments, the existing methods are prone to becoming trapped in a local optimum affected by uneven/open point distributions, which shows that measured points incline toward dense areas. However, in VMM, there is little inclination regardless of the increase in the number of measuring defects. In the second set of experiments, sensitivity to varying noise is tested. The results show that VMM helps prevent unstable sliding in the presence of Gaussian noise. In the third set of experiments, we compare convergence speed and convergence stability under different initial positions. It is verified that VMM exhibits the quadratic convergence. Finally, a set of artificial experiments is implemented, revealing that the proposed method is appropriate for use in automated manufacturing processes such as geometric inspection and allowance distribution. Note to Practitioners—Measuring defects usually occur when using a scanning device to obtain the measured points of a workpiece. Weakening the effect of measuring defects on matching results is critical to promoting manufacturing automation. This paper proposes a new method called variance-minimization matching (VMM) that considers measuring defects. In the first part of this paper, the modeling and theoretical analysis of VMM were introduced. In the second part of this paper, simulated experiments are performed to verify the feasibility of VMM in addressing uneven/open point distributions, measuring noise, and large initial positions. Next, artificial experiments employing VMM in geometric inspection and allowance distribution are presented. The proposed method also applies to other automated manufacturing processes, such as workpiece localization, deformation analysis, and complex parts repair. [ABSTRACT FROM AUTHOR]

We present a novel framework for rigid point cloud registration. Our approach is based on the principles of mechanics and thermodynamics. We solve the registration problem by assuming point clouds as rigid bodies consisting of particles. Forces can be applied between both particle systems so that they attract or repel each other. These forces are used to cause rigid-body motion of one particle system toward the other, until both are aligned. The framework supports physics-based registration processes with arbitrary driving forces, depending on the desired behaviour. Additionally, the approach handles feature-enhanced point clouds, e.g., by colours or intensity values. Our framework is freely accessible for download. In contrast to already existing algorithms, our contribution is to precisely register high-resolution point clouds with nearly constant computational effort and without the need for pre-processing, sub-sampling or pre-alignment. At the same time, the quality is up to 28 percent higher than for state-of-the-art algorithms and up to 49 percent higher when considering feature-enhanced point clouds. Even in the presence of noise, our registration approach is one of the most robust, on par with state-of-the-art implementations. [ABSTRACT FROM AUTHOR]

This work proposes an invariant descriptor and a pipeline for the registration of surface range images based on segmentation/reconstruction making use of an edge detection technique combined with a clustering technique using mesh decimation. This novel descriptor is applied to contours and it is invariant to similarity transformations including rotation, translation, uniform scale and it is robust to noise. The proposed feature descriptor makes use of corresponding points extracted from two images and a signature label is assigned specifically to a point considering the geometrical distribution of its neighbourhood, reducing possible areas of overlapping and the ambiguity in the search process. The descriptor was evaluated through a series of tests with various object range images. To validate the candidate transformations, the fitting errors between the two range images are evaluated by the iterative closest point algorithm. This study also presents and discusses results from the application of the developed pipeline in a vision sensor mounted on a robot arm specially built as part of a R&D project to acquire range images by laser scanning over the surface of hydraulic turbine blades. The sensor generates 3D surface models to be registered in the 3D coordinate system of the robot controller. [ABSTRACT FROM AUTHOR]

Objective In thoracolumbar spinal instrumentation surgery, pedicle screw fixation is widely used, whereas screw loosening occurs only occasionally over time. It is common to evaluate screw loosening by the radiographic lucent zone around screws, which can neither evaluate loosening quantitatively nor detect slight screw loosening. In the present technical note, we describe a novel assessment technique of screw loosening by generating 3-dimensional screw images from computed tomography data and superposing them in time series. Methods Computed tomography data were exported in digital imaging and communications in medicine dataset and imported to the 3-dimensional computer-aided designing software, by which screws and rods were segmented and outputted in stereolithography (STL) format. The STL files were imported to the software, and registration based on iterative closest point algorithm was performed to assess screw position changes. Results Positional changes on STL in time series were classified into 3 types: 1) no position changes existed in the entire system of screws and rods; 2) position changes existed in the entire system of screws and rods, but no position changes were shown when the left-sided and right-sided screws and rod were separately evaluated; and 3) position changes existed in the left and/or right-sided screws and rods even when evaluated separately. Conclusions This technique enables the quantitative evaluation of screw loosening and loosening between screws and rods. In conjunction with conventional methods of assessing radiographic lucent zone, we are able to obtain more accurate information regarding screw loosening after spinal instrumentation surgery. [ABSTRACT FROM AUTHOR]

This paper presents a method for coarse alignment of point clouds by introducing a new descriptor based on the local curvature. The method is developed for model fitting a CAD model for use in robotic assembly. The method is based on selecting keypoints depending on shape factors calculated from the local covariance matrix of the surface. A descriptor is then calculated for each keypoint by fitting two spheres that describe the curvature of the surface. The spheres are calculated using conformal geometric algebra, which gives a convenient and efficient description of the geometry. The keypoint descriptors for the model and the observed point cloud are then compared to estimate the corresponding keypoints, which are used to calculate the displacement. The method is tested in several experiments. One experiment is for robotic assembly, where objects are placed on a table and their position and orientation is estimated using a 3-D CAD model. Note to Practitioners— 3-D cameras can be used in robotic assembly for recognizing objects, and for determining the position and orientation of parts to be assembled. In such applications, 3-D CAD models will be available for the objects, and point clouds representing each object can be generated for comparison with the observed point clouds from the 3-D camera. It is not straightforward to use existing descriptors in this paper, as the point cloud from the CAD model and the observed point cloud may differ due to different viewpoints and potential occlusions. The method proposed in this paper is intended to be easy to apply to industrial assembly problems where there is a need for a robust estimation of the displacement of an object, either as a coarse estimate for use in grasping or as an initial guess to use in fine registration for demanding assembly operations with close tolerances. The method exploits the curvature of the point clouds to accurately describe the surrounding surface of each point. This method serves as a basis for future industrial implementations. [ABSTRACT FROM AUTHOR]

Pressure sensors are routinely used to monitor pressure in different regions of the body. These measurements are generally short-term as current sensors suffer from drift in accuracy, limiting practical use as a chronic implant. We report on the development of an implanted pressure reference for reducing sensor drift through recalibration, whilst the device is in situ. During a recalibration step, the mechanism generates a characteristic response into the pressure signal. The implanted sensor detects this response to perform on-board offset correction adjustments. Calibration is initiated with an external force, such as a simple finger pressing action. ANSYS modeling was used to adjust diaphragm dimensioning and deflection to induce a detectable correction signal. Testing was performed on a benchtop prototype with a ø10-mm titanium diaphragm. The accuracy of a recalibrated pressure sensor was ±0.15 mmHg. This is easily within the accuracy requirement for intracranial pressure monitoring of ±2 mmHg from the NS28 standard. [ABSTRACT FROM AUTHOR]

Rigid robot pose estimation with an RGB-D camera has attracted substantial research attention recently for the reason that the RGB-D camera can capture RGB and depth information simultaneously. Despite the huge progress that has been made, there are still some unresolved issues like the pose estimation in texture-less or structure-less scenes. Aiming at this problem, this paper presents a robust real-time pose estimation method with an RGB-D camera for texture-less and structure-less scenes. Our contributions are threefold. First, we present an improved ORB algorithm for extracting reliable inliers, in which adaptive threshold setting method of FAST corners decision is proposed for extracting sufficient keypoints. In addition, an effective inliers refinement method, based on motion smoothness consistency constraint, is introduced for obtaining fine inliers. Second, based on the characteristics of RGB-D camera, this paper proposes a novel hybrid reprojection errors optimization model (HREOM) to estimate pose by concurrently minimizing 3D-3D and 3D-2D reprojection errors. Third, we carry out comprehensive experiments on TUM public datasets to demonstrate the robustness, accuracy, and real-time of the proposed system. The quantitative evaluations show that our system can extract sufficient inliers in those extreme scenes. Furthermore, our method performs as good as or better than other state-of-the-art solutions. Notably, our system can operate in the texture-less and structure-less environment, while other methods are prone to failure. [ABSTRACT FROM AUTHOR]

Aligning 2.5D views has been extensively explored in the past decades, where most prior works have concentrated on object data with complex structures. This paper presents a method to align real-word scene scans with challenging features such as noise, poor geometric information, and highly repeatable patterns. Our method consists of two modules: pairwise and multiview alignments. Key to the proposed pairwise alignment method is the rotational contour signature geometric feature and voting-based correspondence selection algorithm. The former promises strong discriminative power for 2.5D scene data, while the latter affords high-quality correspondences via a voting process for all raw feature matches using L2 distance and point pair affinity constraints. For the multiview alignment method, we first use a connected graph algorithm to establish the connections of all 2.5D views for coarse merging; then, we propose a shape-growing iterative closest point algorithm for further refinement. Experiments are conducted on scene point cloud datasets addressing both the indoor and outdoor scenarios, whereby we demonstrate that the proposed pairwise alignment method clearly outperforms the state of the art. Moreover, the proposed multiview alignment method manages to put multiple unordered 2.5D scene fragments into a unified coordinate system automatically, accurately, and efficiently. [ABSTRACT FROM AUTHOR]

The autonomous operation of industrial robots with minimal human supervision has always been in high demand. To prepare the autonomous operation of a car part spray painting robot, novel object detection, and pose estimation algorithms have been developed in this paper. The object detection part used principal components analysis (PCA) to reduce the dimension of three-dimensional (3-D) point cloud to 2-D binary image. Distance measure between the auto and cross correlation of the binary features was established to find out the similarity between them. Resultantly, the type of auto part was successfully obtained. Furthermore, iterative closest point (ICP) algorithm was used to estimate the pose difference of the auto part with respect to the camera reference frame, which was mounted on the robot. An issue with ICP's lack of robustness to local minimum was solved by the combination of ICP and genetic algorithm (GA). This allowed the optimization of pose error and addressed the problem of local minimum entrapment in ICP. For experimental validation: the proposed object recognition pipeline was implemented in both serial and parallel programming paradigms. The results were obtained for the acquired point clouds of side body car parts and compared with the major 3-D object detection systems in terms of computational cost. Pose estimation error was calculated with both ICP and the modified point set registration schemes, and it was shown to be decreasing in the case of later. All shown results supported the research claims. [ABSTRACT FROM AUTHOR]

Hydrocephalus is a medical condition, where excessive accumulation of CerebroSpinal Fluid leads to an abnormal increase of intracranial pressure (ICP). Early and timely detection of elevated ICP is very important to mitigate symptoms during the entire life of the patient to avoid headaches, mental disability, and death caused by abnormal increased ICP. We describe our fabricated active capacitance pressure for continuous ICP measurement. The active sensor consists of two metal plates, which transduces the pressure change to capacitance change. We introduced a novel special connection method between the inside-bonded metal plates to external sensor electronics. We tested biocompatibility of the sensor materials in a sheep. We developed prototypes, and in vitro measurement results demonstrate that the active capacitive sensor achieves good performance: 550–900 mmHg measurement range, 0.094% accuracy, 0.022 mmHg (0.001 pF) resolution, and from ±0.136 to ±0.5 mmHg three months long-term drift for 10 different capacitive pressure sensors. Here, 0.5 mmHg corresponds to 0.14% full-scale output with a full range of 550 to 900 mmHg. [ABSTRACT FROM AUTHOR]

This paper reviews the classical problem of free-form curve registration and applies it to an efficient RGB-D visual odometry system called Canny-VO, as it efficiently tracks all Canny edge features extracted from the images. Two replacements for the distance transformation commonly used in edge registration are proposed: approximate nearest neighbor fields and oriented nearest neighbor fields. 3-D–2-D edge alignment benefits from these alternative formulations in terms of both efficiency and accuracy. It removes the need for the more computationally demanding paradigms of data-to-model registration, bilinear interpolation, and subgradient computation. To ensure robustness of the system in the presence of outliers and sensor noise, the registration is formulated as a maximum a posteriori problem and the resulting weighted least-squares objective is solved by the iteratively reweighted least-squares method. A variety of robust weight functions are investigated and the optimal choice is made based on the statistics of the residual errors. Efficiency is furthermore boosted by an adaptively sampled definition of the nearest neighbor fields. Extensive evaluations on public SLAM benchmark sequences demonstrate state-of-the-art performance and an advantage over classical Euclidean distance fields. [ABSTRACT FROM AUTHOR]

Interleaved three-phase voltage source converters (VSCs) are widely applied in industry. The circulating current issue has been the focus so far in literature. However, the common-mode voltage (CMV) generated from interleaved VSCs, which is also a key factor that influences the system's performance, has not yet been studied in detail. In this paper, the impact of pulsewidth modulation (PWM) strategies on the CMV of interleaved two-level VSCs is analyzed. The analysis is conducted based on the double Fourier integral theory and in a generalized way considering an arbitrary number of interleaved converters. Five PWM schemes are considered, including the interleaved sinusoidal PWM (ISPWM), the interleaved space vector modulation (ISVM), the interleaved discontinuous PWM (IDPWM), and the recently proposed interleaved carrier phase-shift PWM (ICPS PWM), and interleaved carrier phase-shift SVM (ICPS SVM). Both magnitude and harmonic content of CMV are examined for each method. The analysis reveals that with ISPWM, ISVM, ICPS PWM, and ICPS SVM, the CMV can be reduced. In contrast, interleaving has little impact on the CMV with IDPWM, which is always at VDC/2 due to high low-order harmonics, making it less attractive for drive applications using coupled architecture. For modular architecture, ICPS PWM and ICPS SVM are more desirable considering their low-ZSCC peak value. The study is verified through experimental results. [ABSTRACT FROM AUTHOR]

Rigid registration is a fundamental process in many applications that require alignment of different datasets. Iterative closest point (ICP) is a widely used algorithm that iteratively finds point correspondences and updates the rigid transformation. One of the key variants of ICP to its success is the selection of points, which is directly related to the convergence and robustness of the ICP algorithm. Besides uniform sampling, there are a number of normal-based and feature-based approaches that consider normal, curvature, and/or other signals in the point selection. Among them, normal-space sampling (NSS) is one of the most popular techniques due to its simplicity and low computational cost. The rationale of NSS is to sample enough constraints to determine all the components of transformation, but this paper finds that NSS actually can constrain the translational normal space only. This paper extends the fundamental idea of NSS and proposes Dual NSS (DNSS) to sample points in both translational and rotational normal spaces. Compared with NSS, this approach has similar simplicity and efficiency without any need of additional information, but has a much better effectiveness. Experimental results show that DNSS can outperform the normal-based and feature-based methods in terms of convergence and robustness. For example, DNSS can achieve convergence from an orthogonal initial position while no other methods can achieve. Note to Practitioners—ICP is commonly used to align different data to a same coordination system. While NSS is often used to speed up the alignment process by down-sampling the data uniformly in the normal space. The implementation of NSS only has three steps: 1) construct a set of buckets in the normal-space; 2) put all points of the data into buckets based on their normal direction; and 3) uniformly pick points from all the buckets until the desired number of points is selected. The algorithm is simple and fast, so that it is still the common practice. However, the weakness of NSS comes from the reason that it cannot handle rotational uncertainties. In this paper, a new algorithm called DNSS is developed to constrain both translation and rotation at the same time by introducing a dual-normal space. With a new definition of the normal space, the algorithm complexity of DNSS is the same as that of NSS, and it can be readily implemented in all types of application that are currently using ICP. The experimental results show that DNSS has better efficiency, quality, and reliability than both normal-based and feature-based methods have. [ABSTRACT FROM AUTHOR]

The internet banking services are secured with different security techniques and methods. The credentials like username and password are the important security measure to provide protection for banking systems. There is a change to steal the username and password, possibility to access the banking application like net banking from the unauthorized users. To protect unauthorized access towards security and privacy, a new emerging technique called biometric based authentication and identification techniques are followed in the banking sector. The Face Recognition is the technique to analyze the characteristics of human involved in authentication process. Fingerprints, face, hand geometry, voice and iris are the basic behaviour or characteristics of a person. The project deals with totally functional face recognition for verifying characteristics of a human face. With that, it also provides multiparty access systems; the account holder provides privileges to the minimum number of users with valid credentials. ICP algorithm gives high level security in online banking systems than the traditional authentication systems. [ABSTRACT FROM AUTHOR]

This paper presents a dense method for estimating planar motion with a laser scanner. Starting from a symmetric representation of geometric consistency between scans, we derive a precise range flow constraint and express the motion of the scan observations as a function of the rigid motion of the scanner. In contrast to existing techniques, which align the incoming scan with either the previous one or the last selected keyscan, we propose a combined and efficient formulation to jointly align all these three scans at every iteration. This new formulation preserves the advantages of keyscan-based strategies but, is more robust against suboptimal selection of keyscans and the presence of moving objects. An extensive evaluation of our method is presented with simulated and real data in both static and dynamic environments. Results show that our approach is one order of magnitude faster and significantly more accurate than existing methods in all the conducted experiments. With a runtime of about one millisecond, it is suitable for those robotic applications that require planar odometry with low computational cost. The code is available online as a ROS package. [ABSTRACT FROM AUTHOR]