Iterative Point Research Articles

A Low-Cost 3D Mapping System for Indoor Scenes Based on 2D LiDAR and Monocular Cameras

The cost of indoor mapping methods based on three-dimensional (3D) LiDAR can be relatively high, and they lack environmental color information, thereby limiting their application scenarios. This study presents an innovative, low-cost, omnidirectional 3D color LiDAR mapping system for indoor environments. The system consists of two two-dimensional (2D) LiDARs, six monocular cameras, and a servo motor. The point clouds are fused with imagery using a pixel-spatial dual-constrained depth gradient adaptive regularization (PS-DGAR) algorithm to produce dense 3D color point clouds. During fusion, the point cloud is reconstructed inversely based on the predicted pixel depth values, compensating for areas of sparse spatial features. For indoor scene reconstruction, a globally consistent alignment algorithm based on particle filter and iterative closest point (PF-ICP) is proposed, which incorporates adjacent frame registration and global pose optimization to reduce mapping errors. Experimental results demonstrate that the proposed density enhancement method achieves an average error of 1.5 cm, significantly improving the density and geometric integrity of sparse point clouds. The registration algorithm achieves a root mean square error (RMSE) of 0.0217 and a runtime of less than 4 s, both of which outperform traditional iterative closest point (ICP) variants. Furthermore, the proposed low-cost omnidirectional 3D color LiDAR mapping system demonstrates superior measurement accuracy in indoor environments.

A modified projection algorithm for solving nonmonotone variational inequalities without Lipschitz continuity

ABSTRACT In this paper, we first present a modified double projection algorithm (MDPA for short) for solving variational inequality problems (VIP for short) without monotonicity. Compared with the algorithm of Ye and He [Comput Optim Appl. 2015;60(1):141–150], the next iterate point of MDPA is generated by projecting the current iterate point onto the intersection of the feasible set and only one half-space. Hence, MDPA can save the computational cost of computing the next iterate point. Moreover, we present a new MDPA (NMDPA for short) to decrease the total number of iterations of MDPA. The computational cost of NMDPA is the same as that of MDPA in each iteration. The global convergence of both MDPA and NMDPA is established under the same assumption that the underlying mapping is continuous and the solution set of its dual variational inequality is nonempty. Numerical experiments show that NMDPA can accelerate MDPA for solving nonmonotone VIP from the total number of iterative point of view and the CPU time point of view. Moreover, NMDPA is more efficient than Algorithm 1 of Dinh et al. [Numer Algorithms. 2022;90(4):1715–1734] from the CPU time point of view and the total number of iterations point of view.

Preliminary application study of digital technology for constructing three-dimensional facial symmetry reference planes in anterior dental esthetic restoration

Objective: To explore the impact of digital technology in constructing a three-dimensional (3D) symmetry reference plane (SRP) for esthetic restoration of anterior teeth and to evaluate its clinical applicability. Methods: A cross-sectional study was conducted from February to May 2024, involving 20 patients [11 males and 9 females, aged (36.8±11.4) years] who underwent anterior esthetic restorations at the Department of Stomatology, People's Hospital of Ningxia Hui Autonomous Region. Symmetrical reference planes of patients' 3D facial models were constructed using three different algorithms: weighted Procrustes analysis (WPA), Procrustes analysis (PA) based on the ontology-mirror correlation method, and iterative closest point (ICP). The SRP defined by an associate chief physician served as the control (true-value group). The angular errors between each algorithm group and the true-value group were compared. The optimal algorithm was selected and combined with a three-dimensional digital smile design (DSD) to create virtual patients, followed by designing anterior restorations. The visual analogue scale (VAS) was used by patients to score the aesthetic restoration results of the conventional design (control group) and the algorithm-based design (algorithm group). Results: The angular errors of the WPA, PA, and ICP groups were 1.43°±0.66°, 1.82°±0.88°, and 4.74°±2.03° respectively, with statistically significant differences among the groups (F=41.10, P<0.001). Pairwise comparisons showed that the WPA group had significantly smaller angular errors compared to the PA and ICP groups (P<0.05). The VAS scores for aesthetic restoration were significantly higher in the algorithm group (8.09±0.74) compared to the control group (6.30±1.38) (t=-5.49, P<0.001). Conclusions: The SRP constructed using the WPA algorithm demonstrated minimal angular error when compared to the expert-defined SRP and is considered the optimal choice in clinical practice, yielding high patient satisfaction.

Blind source separation algorithm for biomedical signal based on lie group manifold

Independent Component Analysis (ICA) is a powerful tool for solving blind source separation problem in biomedical engineering. The traditional ICA algorithm ignores the Lie group structure of constrained matrix manifold. In this paper, a gradient descent algorithm on Lie group manifold is proposed based on the geometric framework of optimization algorithm on Riemann manifold. Firstly, the orthogonal constraint separation matrices are regarded as a Lie group manifold, and the gradient of ICA objective function on the Lie group manifold is given by using Riemann metric; Secondly, the geodesic equation of the current iteration point along the gradient descent direction is calculated; Finally, a new iteration point is obtained by moving a certain step along the geodesic line, meanwhile, the step length can be adjusted adaptively. Simulation results show that the gradient algorithm on Lie group manifold is feasible for blind Source Separation, and its performance (convergence speed, stability and error) is better than other algorithms.

Automated Weld Path Generation in Cluttered Environments Using Segmentation and Iterative Closest Point Workpiece Localization

Abstract A significant amount of manufacturing is performed by small to medium enterprises (SMEs), but these manufacturers often have lower adoption rates of automation. The cost and complexity associated with traditional robot systems slow the adoption of robotic welding operations for SMEs. The recent increase in collaborative robotic welding systems is bridging the gap, however, by reducing the complexity of installing, maintaining, and training operators to perform weld operations with collaborative robotics. These systems add flexibility in the range of operator use and ease of deployment. These systems however still rely on kinematic registration between the robot-mounted torch and workpiece on any open-loop weld. This requires precise placement of the workpiece prior to performing a weld. This work will discuss a method for part identification and registration in a welding task as a step toward automated weld path generation. The method is based on lower-resolution 3D cameras (RGBD cameras) using a combination of color and depth information. This information is used to both identify the workpiece within a workspace that may have other, non-workpiece items, and then provide registration or localization information of the workpiece within a resolution that could allow follow-on near-position strategies to achieve final weld path identification.

Depth gate tracking method based on historical sounding results in MBES

Gate tracking technology plays a crucial role in the autonomous detection of multi-beam echo sounding. The quality of the tracking algorithm directly impacts the quality of the estimate data. Here, we propose a new method for depth gate tracking in multi-beam echo sounding based on the assumption that historical sounding results are geometrically similar to the current ping results. Considering Hausdorff distance measures how far two subsets of a metric space are from each other. An evaluation method based on the concept of similarity distance that relies on the Modified Hausdorff distance is proposed here to measure the similarity between the detection results of different pings and determine the maximum number of historical pings that can be tracked. Then, this model is combined with the iterative closest point registration algorithm to align the bathymetric results of historical pings to the current ping, eliminating the need for prior registration. The registered data are used to initialize the particle distribution in the tracking particle filter and a distance-weighted importance function is established for each beam. Validation on measured data has shown that the proposed method is effective in tracking seabed topography and providing stable gate tracking results.

Calibration of multi-robot coordinates for collaborative wire arc additive manufacturing using cross-source 3D point cloud models

To increase the deposition deficiency and extend the working range of a single actuator, multi-robot collaborative wire arc additive manufacturing (MRC-WAAM) is proliferating in industry. Implementing an MRC-WAAM system should ensure precise transformations between a part frame and multiple robot work frames (RWFs) to facilitate fabrication quality. This paper proposes a novel calibration method for calculating the spatial correlation of RWFs by registering readings of multiple robot-carried 3D sensors. After formulating the influence of both translational and rotational errors, the principles and implementation are presented. To deal with the heterogeneity of cross-source point cloud models, an improved iterative closest point algorithm is articulated. The results of validation show that the proposed method can achieve high accuracy in orientation consistency of RWFs with an error of 0.091 deg, which is superior to the current state-of-the-art. This method can alleviate the consequences caused by the inconformity of multi-robot frames.

A Stochastic Inertial Limited Memory BFGS Algorithm Based on the Mathematical Model of Rural Pipeline Network and its Application in Machine Learning

Abstract Stochastic algorithms are critical in addressing complex rural pipe networks and non-convex stochastic optimization problems. With the development of artificial intelligence, large-scale optimization problems that cannot be solved effectively by traditional optimization methods have emerged. Therefore, stochastic optimization algorithms are rapidly developing in the field of optimization. This paper introduces an inertial extrapolation stochastic BFGS (IESBFGS) algorithm, an innovative amalgamation of the inertial extrapolation technique and the finite memory quasi-Newton algorithm to solve nonconvex stochastic optimization problems. Firstly, the inertial extrapolation technique is employed to track the iteration point to the optimal x-value. Second, it is combined with a finite-memory proposed Newton algorithm thereby increasing the convergence speed. Then, the superiority of IESBFGS is verified by comparing the experimental performance with other better algorithms on machine learning SVM model and ERM model. Finally, it is shown that the algorithm offers good prospects for solving nonconvex problems.

An Improved Non-Monotonic Adaptive Trust Region Algorithm for Unconstrained Optimization

The trust region method is an effective method for solving unconstrained optimization problems. Incorrectly updating the rules of the trust region radius will increase the number of iterations and affect the calculation efficiency. In order to obtain an effective radius for the trust region, an adaptive radius updating criterion is proposed based on the gradient of the current iteration point and the eigenvalue of the Hessian matrix which avoids calculating the inverse of the Hessian matrix during radius updating. This approach reduces the computation time and enhances the algorithm’s performance. On this basis, we apply adaptive radius and non-monotonic techniques to the trust region algorithm and propose an improved non-monotonic adaptive trust region algorithm. Under proper assumptions, the convergence of the algorithm is analyzed. Numerical experiments confirm that the suggested algorithm is effective.

Position Normalization of Propellant Grain Point Clouds

Point cloud data obtained from scanning propellant grains with 3D scanning equipment exhibit positional uncertainty in space, posing significant challenges for calculating the relevant parameters of the propellant grains. Therefore, it is essential to normalize the position of each propellant grain’s point cloud. This paper proposes a normalization algorithm for propellant grain point clouds, consisting of two stages, coarse normalization and fine normalization, to achieve high-precision transformations of the point clouds. In the coarse normalization stage, a layer-by-layer feature points detection scheme based on k-dimensional trees (KD-tree) and k-means clustering (k-means) is designed to extract feature points from the propellant grain point cloud. In the fine normalization stage, a rotation angle compensation scheme is proposed to align the fitted symmetry axis of the propellant grain point cloud with the coordinate axes. Finally, comparative experiments with iterative closest point (ICP) and random sample consensus (RANSAC) validate the efficiency of the proposed normalization algorithm.

Performance evaluation of image co-registration methods in photoacoustic mesoscopy of the vasculature

Objective:The formation of functional vasculature in solid tumours enables delivery of oxygen and nutrients, and is vital for effective treatment with chemotherapeutic agents. Longitudinal characterisation of vascular networks can be enabled using mesoscopic photoacoustic imaging, but requires accurate image co-registration to precisely assess local changes across disease development or in response to therapy. Co-registration in photoacoustic imaging is challenging due to the complex nature of the generated signal, including the sparsity of data, artefacts related to the illumination/detection geometry, scan-to-scan technical variability, and biological variability, such as transient changes in perfusion. To better inform the choice of co-registration algorithms, we compared five open-source methods, in physiological and pathological tissues, with the aim of aligning evolving vascular networks in tumours imaged over growth at different time-points.Approach:Co-registration techniques were applied to 3D vascular images acquired with photoacoustic mesoscopy from murine ears and breast cancer patient-derived xenografts, at a fixed time-point and longitudinally. Images were pre-processed and segmented using an unsupervised generative adversarial network. To compare co-registration quality in different settings, pairs of fixed and moving intensity images and/or segmentations were fed into five methods split into the following categories: affine intensity-based using 1)mutual information (MI) or 2)normalised cross-correlation (NCC) as optimisation metrics, affine shape-based using 3)NCC applied to distance-transformed segmentations or 4)iterative closest point algorithm, and deformable weakly supervised deep learning-based using 5)LocalNet co-registration. Percent-changes in Dice coefficients, surface distances, MI, structural similarity index measure and target registration errors were evaluated.Main results:Co-registration using MI or NCC provided similar alignment performance, better than shape-based methods. LocalNet provided accurate co-registration of substructures by optimising subfield deformation throughout the volumes, outperforming other methods, especially in the longitudinal breast cancer xenograft dataset by minimising target registration errors.Significance:We showed the feasibility of co-registering repeatedly or longitudinally imaged vascular networks in photoacoustic mesoscopy, taking a step towards longitudinal quantitative characterisation of these complex structures. These tools open new outlooks for monitoring tumour angiogenesis at the meso-scale and for quantifying treatment-induced co-localised alterations in the vasculature.

An automatic method of siltation depth detection and 3D modeling in water-filled sewer pipelines based on sonar point clouds

Underground sewer pipeline inspection is essential for ensuring the safety and sustainability of urban infrastructure and improving residents’ quality of life. Traditional sewer pipeline inspection methods incur high manual costs. Therefore, this paper proposes an automated siltation depth identification and three-dimensional pipeline model reconstruction using an iterative farthest point removal fitting process based on a sonar point cloud. This method includes interpreting raw sonar data into point cloud data, point cloud preprocessing, iterative farthest point removal fitting process to fit pipeline profile and reverse interpolation of pipeline slices for three-dimensional model reconstruction. This method is validated in real-world applications, successfully calculating and annotating silt depth for pipeline slice profiles. The quantitative analysis shows an error in the estimated siltation proportion fluctuating within approximately 1%. This method also reconstructs the three-dimensional spatial model of siltation in the sewer pipeline, thereby providing effective technical support for engineering applications and reducing manual effort.

A two-step relaxed-inertial derivative-free projection based algorithm for solving standard nonlinear pseudo-monotone equations and logistic regression problems

This paper explores a two-step inertial derivative-free projection method with a relaxation factor γ∈(0,2) for solving nonlinear pseudo-monotone equations. Unlike existing inertial algorithms for the system of nonlinear pseudo-monotone equations, the inertial step of our method involves the current iteration point and the previous two iteration points. In particular, one of the inertial parameters is nonpositive. In the proposed algorithm, the search direction possesses not only the sufficient descent property but also the trust region property, independent of the line search technique. Moreover, we also establish the global convergence and the convergence rate of the algorithm without the Lipschitz continuity of the underlying mapping. Finally, our method provides competitive results on standard nonlinear monotone and pseudo-monotone equations and logistic regression problems compared with two inertial algorithms existing in the literature.

Accurate reconstruction of turbine blade point cloud and multiple point cloud registration based on structured light

In recent years, the industry has increasingly adopted vision-guided laser processing techniques to machine cooling holes in turbine blades, placing higher demands on the accuracy of visual measurement. Therefore, to meet the need for high-precision measurement of the three-dimensional shape of turbine blades in the context of laser processing of film cooling holes, this paper proposes a structured light-based method for precise reconstruction of turbine blade point clouds and multi-point cloud registration. Specifically, to improve the accuracy of point cloud reconstruction, an improved phase matching and reconstruction method based on epipolar constraints is proposed. To enhance the accuracy and stability of multi-point cloud registration, a weighted iterative closest point (ICP) method combined with a mark point registration strategy is proposed, optimizing the stability of the ICP algorithm and achieving high-quality multiple point cloud stitching. Experimental results validate the effectiveness and accuracy of the proposed method.

Particle tracking-aided digital volume correlation for clay–sand soil mixtures

In this study, a novel, interdisciplinary method is introduced that merges fundamental geomechanics with computer vision to develop an advanced hybrid feature-aided digital volume correlation (DVC) technique. This technique is specifically engineered to measure and compute the full-field strain distribution in fine-grained soil mixtures. A clay–sand mixture specimen composed of quartz sand particles and kaolinite was created. Its mechanical properties and deformation behaviour were then tested using a mini-triaxial apparatus, combined with micro-focus X-ray computed tomography (μCT). The CT slices underwent image processing for denoising, segmentation of distinct phases, reconstruction of sand particles and feature extraction within the soil specimen. The proposed approach incorporated a two-step particle tracking method, which initially uses particle volume and surface area features to establish a preliminary matching list for a reference particle and then use the iterative closest point method for precise target particle matching. The soil specimen's initial displacement field was then mapped onto the DVC method's grid, and further refined through sub-voxel registration by way of a three-dimensional inverse compositional Gauss–Newton algorithm. The proposed method's effectiveness and efficiency were validated by accurately calculating the displacement and strain fields of the soil mixture sample, and comparing the results with those from a traditional DVC method. Given the soil's compositional and microstructural characteristics, these image-matching techniques can be integrated to create a versatile, efficient, and robust DVC system, suitable for a variety of soil mixture types.

An improved SLAM algorithm for substation inspection robot based on the fusion of IMU and visual information

In the past, manual inspection was often used for equipment inspection in indoor environments such as substation rooms and chemical plant rooms. This way often accompanies high labor intensity, low inspection efficiency, and low safety, which is difficult to meet the increasingly stringent requirements of indoor equipment operation and maintenance management. For dealing with these issues, a VIORB-SLAM2 algorithm based on the integration of IMU and visual information, was proposed by this paper. Firstly, the IMU data and image data were integrated to restore scale information of cameras, and then an error function was established to enhance the algorithm’s robustness. Secondly, in order to improve the accuracy of the algorithm, the random sampling consensus method was used to eliminate the wrong matching points in feature point matching, and the normalized cross-correlation matching was employed to constrain key frame matching conditions. Finally, through the iterative closest point method to stitch the point clouds, a dense map for navigation was constructed. The experimental results show that the algorithm designed by this paper has solved the shortcomings of applying the ORB-SLAM2 algorithm to indoor inspection robots while achieving high positioning accuracy, which can be combined with other algorithms in the field of artificial intelligence for object detection and semantic map construction in the future.

An efficient iterative pseudo point elimination technique to represent the shape of the digital image boundary

An efficient iterative pseudo point elimination technique to represent the shape of the digital image boundary

ICP registration with SHOT descriptor for arresters point clouds

Abstract Arresters are one of the critical components of the power system.&amp;#xD;However, due to the arrester’s regular and uniform umbrella skirt, both traditional&amp;#xD;manual detection methods and existing computer vision approaches exhibit limitations&amp;#xD;in accuracy and efficiency. This paper proposes an automatic, robust, efficient arrester&amp;#xD;point cloud registration method to address this problem. First, a robotic arm&amp;#xD;maneuvers a depth camera to capture point cloud data from various perspectives.&amp;#xD;Then, the fast global registration (FGR) point cloud coarse registration method&amp;#xD;based on the signature of histograms of orientations (SHOT) descriptor to produce&amp;#xD;preliminary registration results. This result is ultimately used as the initial value of&amp;#xD;the improved iterative closest point (ICP) algorithm to refine the registration further.&amp;#xD;Experimental results on various data sets collected from arrester and public data sets&amp;#xD;show that the algorithm’s root mean square error(RMSE) is less than 0.1mm, meeting&amp;#xD;the requirements of the engineering application of arrester detection.

RD-SLAM: Real-Time Dense SLAM Using Gaussian Splatting

Simultaneous localization and mapping (SLAM) is fundamental for intelligent mobile units to perform diverse tasks. Recent work has shown that integrating neural rendering and SLAM showed promising results in photorealistic environment reconstruction. However, existing methods estimate pose by minimizing the error between rendered and input images, which is time-consuming and cannot be run in real-time, deviating from the original intention of SLAM. In this paper, we propose a dense RGB-D SLAM based on 3D Gaussian splatting (3DGS) while employing generalized iterative closest point (G-ICP) for pose estimation. We actively utilize 3D point cloud information to improve the tracking accuracy and operating speed of the system. At the same time, we propose a dual keyframe selection strategy and its corresponding densification method, which can effectively reconstruct new observation scenes and improve the quality of previously constructed maps. In addition, we introduce regularization loss to address the scale explosion of the 3D Gaussians and over-elongate in the camera viewing direction. Experiments on the Replica, TUM-RGBD, and ScanNet datasets show that our method achieves state-of-the-art tracking accuracy and runtime while being competitive in rendering quality.

Approach angle estimation method for ships based on deep learning

Accurately estimating the ship's approach angle is crucial during berthing, or passing through navigational structures. It forms the foundation for ship navigation decisions. This study introduces the LiDAR point cloud-based ship pose perception network (PP-Net), a novel deep learning-based method for acquiring ship approach angles. Addressing the challenge of manually annotating approach angles, the ship pose perception method based on point cloud feature distribution (FD) was proposed to obtain initial angle estimates. The max-pooling function was utilized to address the disorder of point clouds, and a multi-resolution feature extractor was employed to enhance the network's robustness in extracting features from inconsistently dense point clouds. By using iterative farthest point sampling (IFPS), datasets with varying numbers of point clouds were standardized to a fixed number of points, thereby normalizing input dimensions and improving computational efficiency. Additionally, a novel loss function was proposed to improve the model's predictive accuracy. Experimental validation conducted in the scenario of ships entering ship lifts demonstrates the effectiveness of the proposed method. The outcomes can support safe navigation for ships in restricted waterways.