Iterative Closest Point Algorithm Research Articles

Accuracy of facial skeletal surfaces segmented from CT and CBCT radiographs

The accuracy of three-dimensional (3D) facial skeletal surface models derived from radiographic volumes has not been extensively investigated yet. For this, ten human dry skulls were scanned with two Cone Beam Computed Tomography (CBCT) units, a CT unit, and a highly accurate optical surface scanner that provided the true reference models. Water-filled head shells were used for soft tissue simulation during radiographic imaging. The 3D surface models that were repeatedly segmented from the radiographic volumes through a single-threshold approach were used for reproducibility testing. Additionally, they were compared to the true reference model for trueness measurement. Comparisons were performed through 3D surface approximation techniques, using an iterative closest point algorithm. Differences between surface models were assessed through the calculation of mean absolute distances (MAD) between corresponding surfaces and through visual inspection of facial surface colour-coded distance maps. There was very high reproducibility (approximately 0.07 mm) and trueness (0.12 mm on average, with deviations extending locally to 0.5 mm), and no difference between radiographic scanners or settings. The present findings establish the validity of lower radiation CBCT imaging protocols at a similar level to the conventional CT images, when 3D surface models are required for the assessment of facial morphology.

An Iterative Closest Point Method for Lidar Odometry with Fused Semantic Features

Lidar sensors play a pivotal role in a multitude of remote sensing domains, finding extensive applications in various sectors, including robotics, unmanned aerial vehicles (UAVs), autonomous driving, and 3D reconstruction, among others. Their significance and versatility in these areas have made them indispensable tools for a wide range of applications. The accuracy of Lidar odometry (LO), which serves as the front end of SLAM, is crucial. In this paper, we propose a novel iterative closest point (ICP) technique that combines semantic features to improve LO precision. First, the semantic segmentation neural network is used to extract the semantic features from each frame of the point cloud. Then, the obtained semantic features assist in extracting the local geometric features of the point cloud. Also, the residual blocks of the ICP algorithm’s least squares are combined with semantic confidence functions to better predict the exact pose. Compared to LOAM, there is an average improvement of 4 cm in accuracy per 100 m. Experimental results illustrate the superiority of the proposed method and indicate that the fusion of semantic features can robustly improve the precision of LO.

Airborne LiDAR Strip Adjustment Method Based on Point Clouds with Planar Neighborhoods

Airborne light detection and ranging (LiDAR) data are increasingly used in various fields such as topographic mapping, urban planning, and emergency management. A necessary processing step in the application of airborne LiDAR data is the elimination of mismatch errors. This paper proposes a new method for airborne LiDAR strip adjustment based on point clouds with planar neighborhoods; this method is intended to eliminate errors in airborne LiDAR point clouds. Initially, standard pre-processing tasks such as denoising, ground separation, and resampling are performed on the airborne LiDAR point clouds. Subsequently, this paper introduces a unique approach to extract point clouds with planar neighborhoods which is designed to enhance the registration accuracy of the iterative closest point (ICP) algorithm within the context of airborne LiDAR point clouds. Following the registration of the point clouds using the ICP algorithm, tie points are extracted via a point-to-plane projection method. Finally, a strip adjustment calculation is executed using the extracted tie points, in accordance with the strip adjustment equation for airborne LiDAR point clouds that was derived in this study. Three sets of airborne LiDAR point cloud data were utilized in the experiment outlined in this paper. The results indicate that the proposed strip adjustment method can effectively eliminate mismatch errors in airborne LiDAR point clouds, achieving a registration accuracy and absolute accuracy of 0.05 m. Furthermore, this method’s processing efficiency was more than five times higher than that of traditional methods such as ICP and LS3D.

An Augmented Reality-Assisted Disassembly Approach for End-of-Life Vehicle Power Batteries

The rapid expansion of the global electric vehicle industry has presented significant challenges in the management of end-of-life power batteries. Retired power batteries contain valuable resources, such as lithium, cobalt, nickel, and other metals, which can be recycled and reused in various applications. The existing disassembly processes rely on manual operations that are time-consuming, labour-intensive, and prone to errors. This research proposes an intelligent augmented reality (AR)-assisted disassembly approach that aims to increase disassembly efficiency by providing scene awareness and visual guidance to operators in real-time. The approach starts by employing a deep learning-based instance segmentation method to process the Red-Green-Blue-Dept (RGB-D) data of the disassembly scene. The segmentation method segments the disassembly object instances and reconstructs their point cloud representation, given the corresponding depth information obtained from the instance masks. In addition, to estimate the pose of the disassembly target in the scene and assess their disassembly status, an iterative closed point algorithm is used to align the segmented point cloud instances with the actual disassembly objects. The acquired information is then utilised for the generation of AR instructions, decreasing the need for frequent user interaction during the disassembly processes. To verify the feasibility of the AR-assisted disassembly system, experiments were conducted on end-of-life vehicle power batteries. The results demonstrated that this approach significantly enhanced disassembly efficiency and decreased the frequency of disassembly errors. Consequently, the findings indicate that the proposed approach is effective and holds promise for large-scale industrial recycling and disassembly operations.

Application of the virtual-fit method for fixed complete denture cases designed on intraoral scans: Effect of cement spacing

ObjectivesTo validate the virtual-fit alignment, analyze the impact of cement spacing on internal/marginal gaps, and correlate results with conventional trueness measures. MethodsFour dental abutment models were scanned using an industrial reference scanner (one time each), Emerald S (three times each), and Medit i700 (three times each) intraoral scanners (IOS). On each IOS scan (n = 24), three complete-arch fixed frameworks were designed with 70 or 140 µm cement space with no marginal space (groups 70 and 140) and 70 µm with an additional 20 µm space, including the margin (group 70+20). Two types of alignment were performed by GOM Inspect software. The reference and IOS scans were aligned through a conventional iterative closest point algorithm (ICP) where the penetration of the two scans was permitted into each other (conventional trueness method). Second, the computer-aided designs were superimposed with the reference scan also using an ICP, but preventing the design from virtual penetration into the model (virtual-fit method). The virtual-fit algorithm was validated by non-penetration alignment of the designs with the IOS scans. Internal and marginal gap was measured between the design and the abutments. The difference between spacing groups was compared by Friedman's test. A statistical correlation (Spearman's Rho Test) was computed between the measured gaps and the conventional trueness method. A significant difference was accepted at p<0.05 after the Bonferroni correction. ResultsThe gaps deviated from the set cement space by 3–13 µm on IOS scans (validation of virtual-fit algorithm). The internal gap of the design on the reference scan was not affected by cement spacing (Emerald S, p = 0.779; Medit i700, p = 0.205). The marginal gap in groups 70 and 70+20 was significantly lower than in group 140 in Emerald S (p<0.05). In Medit i700, it was lower in the 70+20 group than in the group 70 (p<0.01) and in the group 140 (p<0.05). Some Medit i700 scans exhibited high marginal gaps within group 70 but not in groups 70 and 140. The measured gaps correlated significantly (r = 0.51–0.81, p<0.05–0.001) with the conventional trueness but were 2.6–4.6 times higher (p<0.001). ConclusionVirtual-fit alignment can simulate restoration seating. A 20 µm marginal and 90 µm internal spacing could compensate for scan errors up to several hundred micrometers. However, 140 µm internal spacing is counterproductive. The conventional trueness method could only partially predict framework misfit. Clinical significanceThe virtual-fit method can provide clinically interpretable data for intraoral scanners. Emerald S and Medit i700 intraoral scanners are suitable for fabricating complete-arch fixed tooth-supported prostheses. In addition, a slight elevation of spacing at the margin could compensate for moderate inaccuracies in a scan.

Cutting guides in mandibular tumor ablation: Are we as accurate as we think?

PurposeTumor margin status is critical in local tumor recurrence and is a significant prognostic factor in head and neck cancer survival. With the introduction of computer-assisted surgical planning, one of the main challenges is the accurate positioning of the surgical cutting guide but there is limited evidence of the accuracy of the 3D cutting guides in mimicking virtually planned osteotomy. This study evaluates the accuracy of osteotomy lines produced by 3D-printed cutting guides and assesses the overall accuracy of mandibular reconstruction. Material and MethodsThe pre and postoperative 3D models were aligned using an automated surface registration feature based on the iterative closest point algorithm. The differences in osteotomy line deviation, linear and angle measurements, and 3D volume quantification of the pre and post models were measured. ResultsWe included 14 patients (8 men and 6 women with ages ranging from 13 to 75 years) with a segmental mandibular resection who met all of the inclusion criteria. The smallest defect size was 4.4 cm, the largest defect was 12.2 cm, and the average was 7.30 cm +/- 2.80 cm. The average deviation between virtually planned osteotomy and actual surgical osteotomy was 1.52 +/-1.02 mm. No covariates were associated with increased inaccuracy of the 3D-printed cutting guides. ConclusionThe finding of this study suggests that virtual surgical planning is an unambiguous paradigm shift in the predictability of the surgical plan and achievement of the reconstruction goals. The 3D-printed cutting guides are a very accurate and reliable tool in translating virtual ablation plans to an actual surgical resection margin.

Research on Hand–Eye Calibration Accuracy Improvement Method Based on Iterative Closest Point Algorithm

In the functioning of the hand–eye collaboration of an apple picking robot, the accuracy of the hand–eye relationship is a key factor affecting the efficiency and accuracy of the robot’s operation. In order to enhance the low accuracy of traditional hand–eye calibration methods, linear and nonlinear solving methods based on mathematical tools such as quaternions are commonly adopted. To solve the loss of accuracy in decoupling during the linearization solution and to reduce the cumulative error that occurs during nonlinear solutions, a hand–eye calibration method, based on the ICP algorithm, is proposed in this paper. The method initializes the ICP matching algorithm with a solution derived from Tsai–Lenz, and substitutes it for iterative computation, thereby ascertaining a precise hand–eye conversion relationship by optimizing the error threshold and iteration count in the ICP matching process. Experimental results demonstrate that the ICP-based hand–eye calibration optimization algorithm not only circumvents the issues pertaining to accuracy loss and significant errors during solving, but also enhances the rotation accuracy by 13.6% and the translation accuracy by 2.47% compared with the work presented by Tsai–Lenz.

First Ray Mobility in Hallux Rigidus, Hallux Valgus, and Normal Feet Based on Weightbearing Computed Tomography and Three-Dimensional Analysis

Category: Midfoot/Forefoot; Bunion Introduction/Purpose: Both hallux rigidus (HR) and hallux valgus (HV) are conditions that occur in the first ray. Although both are believed to originate from hypermobility of the first ray, they each ultimately result in different clinical feature. Hypermobility of the first ray is strongly involved in the pathogenesis of HV. In contrast, several studies have reported an association between HR and hypermobility of the first ray but few have actually analyzed the nature of that association. We hypothesized that in HR, there would be little rotational movement despite the presence of hypermobility of the first ray. The purpose of this study was to evaluate the first ray mobility in feet with HR, HV, and in healthy feet by using weightbearing and non-weightbearing CT and 3D analysis. Methods: In this case-control study, we examined 17 feet of 11 healthy volunteers with no history of foot disorders and no symptoms such as pain in the foot (control group), 16 feet of 16 patients with HV and 16 feet of 11 patients with HR. First, non- weightbearing foot CT imaging was performed with the participant supine on our original loading device, with the legs extended and the ankle in a neutral position, and then weightbearing CT imaging was performed by applying a load equivalent to body weight. Using an iterative closest point algorithm, each joint in the first ray—the talonavicular joint, the medial cuneonavicular joint, and the first tarsometatarsal (TMT) joint—was aligned using its respective proximal bone. Displacement of the distal bone relative to the proximal bone was quantified in 3D under both weightbearing and non-weightbearing conditions. Results: In the talonavicular joint, the HV group showed significantly greater eversion (p = 0.011) than the control group, and greater dorsiflexion (p = 0.027) and eversion (p &lt; 0.01) than the HR group. In the medial cuneiform joint, the HV group showed significantly greater eversion (p &lt; 0.01) and abduction (p = 0.011) than the control group. In the first TMTl joint, the HV group showed significantly greater dorsiflexion (p = 0.014), inversion (p = 0.028), and adduction (p &lt; 0.01) than the control group, and greater inversion (p &lt; 0.01) and adduction (p &lt; 0.01) than the HR group. The HR group showed significantly greater dorsiflexion (p = 0.026) than the control group. Conclusion: In the present study, hypermobility was observed at the first TMT in both HV and HR. However, the changes in HR were mainly in the sagittal plane, whereas those in HV were in all three planes. This difference may explain the different clinical feature ultimately observed in each condition. Future studies involving comparison of various severity levels and postoperative analysis should elucidate the pathophysiology and contribute to selection of appropriate treatment as well as the development of new treatments for HV and HR.

A Photograph-Based Visualization and Prediction Framework for Radiation-Induced Dermatitis.

This study aimed to suggest a photograph-based prediction system for acute radiation-induced dermatitis (RID), which can be applied to notify patients about the risk of the development of skin discomfort during radiotherapy. The proposed system compared the spatial dose distribution with the RID region using the following methods. Skin photographs of patients were taken using an RGB-depth camera to acquire the shape information of RID. The skin surface data measured from the camera was registered with the shape of the external body contour using an iterative closest point algorithm. Spatial dose distribution of skin was extracted from the external body contour to a depth of 2 mm and projected onto the plane of the skin photograph using a transformation matrix for skin depth data. To compare the spatial distribution of skin dose with the shape of RID, the region of RID in patients' skin was delineated on photographs into three toxicity symptoms referring to the CTCAE criteria grade 1 (skin redness), grade 2 (dry desquamation), and grade 3 (moist desquamation). The degree of overlap between the shape of each RID and skin dose distribution was evaluated using the dice similarity coefficient (DSC). Threshold doses for predicting RID occurrence were estimated by skin isodose lines with the highest DSC. The developed system was validated using data from 19 patients who received volumetric modulated arc therapy for head-neck cancer at a single institution. Threshold doses for RID grades 1, 2, and 3 were estimated using 18, 18, and 2 individual RID labels delineated on skin photographs, respectively. Isodose lines with the highest DSC for RID grades 1, 2, and 3 were calculated as 26.0 Gy, 36.5 Gy, and 54.0 Gy, respectively. A strong overlap (average DSC > 0.6) was observed between isodose skin lines and the shape of RID labels in all RID grades. Assessing the spatial information of skin dose can be helpful in predicting acute RID. The region of RID shows a strong similarity with the skin dose distribution in head-neck patients. Visualization of skin dose on the patient photograph is potent to patient education for preparing the cosmetic discomfort during radiotherapy, which may lead to the improvement of the patient satisfaction in treatment.

Automatic Multiview Alignment of RGB-D Range Maps of Upper Limb Anatomy.

Digital representations of anatomical parts are crucial for various biomedical applications. This paper presents an automatic alignment procedure for creating accurate 3D models of upper limb anatomy using a low-cost handheld 3D scanner. The goal is to overcome the challenges associated with forearm 3D scanning, such as needing multiple views, stability requirements, and optical undercuts. While bulky and expensive multi-camera systems have been used in previous research, this study explores the feasibility of using multiple consumer RGB-D sensors for scanning human anatomies. The proposed scanner comprises three Intel® RealSenseTM D415 depth cameras assembled on a lightweight circular jig, enabling simultaneous acquisition from three viewpoints. To achieve automatic alignment, the paper introduces a procedure that extracts common key points between acquisitions deriving from different scanner poses. Relevant hand key points are detected using a neural network, which works on the RGB images captured by the depth cameras. A set of forearm key points is meanwhile identified by processing the acquired data through a specifically developed algorithm that seeks the forearm's skeleton line. The alignment process involves automatic, rough 3D alignment and fine registration using an iterative-closest-point (ICP) algorithm expressly developed for this application. The proposed method was tested on forearm scans and compared the results obtained by a manual coarse alignment followed by an ICP algorithm for fine registration using commercial software. Deviations below 5 mm, with a mean value of 1.5 mm, were found. The obtained results are critically discussed and compared with the available implementations of published methods. The results demonstrate significant improvements to the state of the art and the potential of the proposed approach to accelerate the acquisition process and automatically register point clouds from different scanner poses without the intervention of skilled operators. This study contributes to developing effective upper limb rehabilitation frameworks and personalized biomedical applications by addressing these critical challenges.

Optical Co-Registration Method of Triaxial OPM-MEG and MRI.

The advent of optically pumped magnetometers (OPMs) facilitates the development of on-scalp magnetoencephalography (MEG). In particular, the triaxial OPM emerged recently, making simultaneous measurements of all three orthogonal components of vector fields possible. The detection of triaxial magnetic fields improves the interference suppression capability and achieves higher source localization accuracy using fewer sensors. The source localization accuracy of MEG is based on the accurate co-registration of MEG and MRI. In this study, we proposed a triaxial co-registration method according to combined principal component analysis and iterative closest point algorithms for use of a flexible cap. A reference phantom with known sensor positions and orientations was designed and constructed to evaluate the accuracy of the proposed method. Experiments showed that the average co-registered position errors of all sensors were approximately 1 mm and average orientation errors were less than 2.5° in the X- and Y orientations and less than 1.6° in the Z orientation. Furthermore, we assessed the influence of co-registration errors on the source localization using simulations. The average source localization error of approximately 1 mm reflects the effectiveness of the co-registration method. The proposed co-registration method facilitates future applications of triaxial sensors on flexible caps.

Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction

For iterative closest point (ICP) algorithm, the initial position and the number of iterations are needed in registration. At the same time, the ICP algorithm is easy to fall into local convergence and convergence speed is slow. By constructing K-D tree to search neighborhood points and artificially set threshold, plane fitting is carried out, the on-time point cloud to be deployed is separated from the complex background, and statistical analysis is used to calculate the distance between the point cloud and the neighborhood point to quickly remove the invalid point cloud. The surface equation is set to calculate the tangent plane of point cloud normal vector and each normal vector, and the local coordinate system is constructed. The angle between adjacent vectors and the local coordinate system is calculated to determine the feature point set of edge contour. According to the covariance matrix of the feature points set, the principal feature component is obtained, the principal axis direction of the two sets of point clouds is found, and the rotation matrix and the displacement vector are obtained. Finally, GICP precise registration of point cloud is carried out according to initial pose parameters and rigid body transformation matrix obtained by maximum likelihood estimation method. The results show that the optimized algorithm can effectively avoid local convergence. Compared with the traditional ICP algorithm, when the algorithm achieves the same registration accuracy in the public dataset experiment, the registration speed is on average 44.82% faster and the overlap rate is on average 15.26% higher. In the real dataset experiment, the registration speed is on average 59.04% faster, the registration accuracy is on average 30.24% higher and the overlap rate is on average 10.61% higher. This shows that the optimization algorithm is superior to the traditional ICP algorithm in registration accuracy and convergence speed.

Real-Time Bucket Pose Estimation Based on Deep Neural Network and Registration Using Onboard 3D Sensor.

Real-time and accurate bucket pose estimation plays a vital role in improving the intelligence level of mining excavators, as the bucket is a crucial component of the excavator. Existing methods for bucket pose estimation are realized by installing multiple non-visual sensors. However, these sensors suffer from cumulative errors caused by loose connections and short service lives caused by strong vibrations. In this paper, we propose a method for bucket pose estimation based on deep neural network and registration to solve the large registration error problem caused by occlusion. Specifically, we optimize the Point Transformer network for bucket point cloud semantic segmentation, significantly improving the segmentation accuracy. We employ point cloud preprocessing and continuous frame registration to reduce the registration distance and accelerate the Fast Iterative Closest Point algorithm, enabling real-time pose estimation. By achieving precise semantic segmentation and faster registration, we effectively address the problem of intermittent pose estimation caused by occlusion. We collected our own dataset for training and testing, and the experimental results are compared with other relevant studies, validating the accuracy and effectiveness of the proposed method.

Terahertz 3D point cloud imaging for complex targets.

The reconstruction of complex targets using terahertz technology is often hindered by diffraction and interference of electromagnetic waves, leading to the loss of fine target details. In this research article, we have introduced a terahertz synthetic aperture radar (SAR) imaging method that integrates an iterative closest point (ICP) algorithm, referred to as SAR-ICP, to achieve accurate reconstruction of intricate target structures. To accomplish this, multiple sets of point cloud data are acquired by varying the illumination viewpoint. The ICP algorithm is then employed to align and fuse these datasets, resulting in the generation of high-quality three-dimensional (3D) images. The experimental results validate the effectiveness of the proposed SAR-ICP method. The information entropy of the reconstructed 3D image using the SAR-ICP is approximately 0.05 times that of the conventional SAR method, indicating a superior image quality. In the future, we anticipate the widespread application of this method in areas such as security inspection, non-destructive testing, and other complex scenarios.

A Stereo-Vision-Based Spatial-Positioning and Postural-Estimation Method for Miniature Circuit Breaker Components

This paper proposes a stereo-vision-based method that detects and registers the positions and postures of muti-type, randomly placed miniature circuit breaker (MCB) components within scene point clouds acquired by a 3D stereo camera. The method is designed to be utilized in the flexible assembly of MCBs to improve the precision of gripping small-sized and complex-structured components. The proposed method contains the following stages: First, the 3D computer-aided design (CAD) models of the components are converted to surface point cloud models by voxel down-sampling to form matching templates. Second, the scene point cloud is filtered, clustered, and segmented to obtain candidate-matching regions. Third, point cloud features are extracted by Intrinsic Shape Signatures (ISSs) from the templates and the candidate-matching regions and described by Fast Point Feature Histogram (FPFH). We apply Sample Consensus Initial Alignment (SAC-IA) to the extracted features to obtain a rough matching. Fourth, fine registration is performed by employing Iterative Closest Points (ICPs) with a K-dimensional Tree (KD-tree) between the templates and the roughly matched targets. Meanwhile, Random Sample Consensus (RANSAC), which effectively solves the local optimal problem in the classic ICP algorithm, is employed to remove the incorrectly matching point pairs for further precision improvement. The experimental results show that the proposed method achieves spatial positioning errors smaller than 0.2 mm and postural estimation errors smaller than 0.5°. The precision and efficiency meet the requirements of the robotic flexible assembly for MCBs.

Scalable hybrid adjustment of images and LiDAR point clouds

Accurately orientated and matched images and light detection and ranging (LiDAR) point clouds are becoming the new standard for many applications in geomatics. Large data volumes and different observation types make scalability and efficient optimization challenging, particularly for matching based on variants of the iterative closest point algorithm. Here, we present a method to address this by temporally segmenting the pre-processed global navigation satellite system/inertial navigation system solution and representing LiDAR data as voxels with metadata. The efficiency, accuracy, and scalability of different voxel sizes have been assessed in this study. Hybrid adjustment of image tie-points and voxels from LiDAR data optimized the trajectory and sensor corrections for both LiDAR scanners and cameras in the presented experiment. A little over one billion LiDAR points and ∼3000 images were matched in <1.5 h and showed high accuracy in the range of a few millimeters. This accuracy is comparable to the existing methods for hybrid image and LiDAR adjustment.

Affine Iterative Closest Point Algorithm Based on Color Information and Correntropy for Precise Point Set Registration.

In this paper, we propose a novel affine iterative closest point algorithm based on color information and correntropy, which can effectively deal with the registration problems with a large number of noise and outliers and small deformations in RGB-D datasets. Firstly, to alleviate the problem of low registration accuracy for data with weak geometric structures, we consider introducing color features into traditional affine algorithms to establish more accurate and reliable correspondences. Secondly, we introduce the correntropy measurement to overcome the influence of a large amount of noise and outliers in the RGB-D datasets, thereby further improving the registration accuracy. Experimental results demonstrate that the proposed registration algorithm has higher registration accuracy, with error reduction of almost 10 times, and achieves more stable robustness than other advanced algorithms.

Computed Tomography and 3-D Face Scan Fusion for IoT-Based Diagnostic Solutions

In clinical diagnosis, multimodal medical image fusion is meaningful and necessary, for the reason that some diseases need to be diagnosed in combination with the situation of different tissues of patients. Spiral computed tomography realizes the high-precision and smooth reconstruction of bone tissue, while it can not represent the color and texture information in soft tissue reconstruction with high accuracy. The face scan precisely records the color and shape of the maxillofacial region. The diagnosis of some diseases (like cavernous hemangioma and jaw deformity caused by idiopathic condylar resorption) needs to combine the information of maxillofacial soft tissue and bone, so it is of great significance to fuse spiral CT and face scan images. In this paper, a novel intelligent IoT scene is proposed: a multimodal medical images acquisition and fusion system, and by combining CT machine and face scan equipment, the CT and face scan of patients can be synchronously collected. Deep point neural networks are used to extract feature points and a threshold iterative closest point algorithm performing registration with deep feature points and contributed region segmentation is applied. Finally, high-precision fused modal data is output at the mobile terminal to facilitate diagnosis and analysis and improve the efficiency of doctor-patient communication. Quantitative experiments show promising results, and clinical experiments prove that our method enables patients and doctors to better understand the state of an illness and improves the efficiency of doctor-patient communication.

Adaptation analysis of produced 3D models from UAV-SLAM and UAV-TLS data combinations

Photogrammetric techniques are widely used to represent the characteristics of historical buildings in the digital environment in the closest way to reality. Terrestrial photogrammetric methods have needed different alternatives in recent years to produce 3D models that offer high reality. In recent years, producing point data with the Terrestrial Laser Scanner (TLS), Unmanned Aerial Vehicles (UAV) images, and alternative methods such as the Simultaneous Localization and Mapping (SLAM) algorithm has become popular. Aligning point data from different approaches and making 3D models create new problems. Primarily, it is necessary to investigate the operations performed manually by the operator over time. Also, it is needed to explore the integration of automatic algorithms such as Iterative Closest Point (ICP) in terms of accuracy. In this research, point data of the Dokuz Historical Bridge was produced using different aligning techniques manually and automatically with the ICP algorithm. The assessment has been done from the results of combined point data over time and accuracy. In both UAV-TLS and UAV-SLAM aligning procedures, spatial accuracy was determined as 2.8cm and 4cm, respectively, in the operations performed by the operator and 46cm and 12cm in the procedures performed automatically by ICP. As a result, it was determined that the operators combined produced better findings, especially in research including coordinate transform.

A Global Structure and Adaptive Weight Aware ICP Algorithm for Image Registration

As an important technology in 3D vision, point-cloud registration has broad development prospects in the fields of space-based remote sensing, photogrammetry, robotics, and so on. Of the available algorithms, the Iterative Closest Point (ICP) algorithm has been used as the classic algorithm for solving point cloud registration. However, with the point cloud data being under the influence of noise, outliers, overlapping values, and other issues, the performance of the ICP algorithm will be affected to varying degrees. This paper proposes a global structure and adaptive weight aware ICP algorithm (GSAW-ICP) for image registration. Specifically, we first proposed a global structure mathematical model based on the reconstruction of local surfaces using both the rotation of normal vectors and the change in curvature, so as to better describe the deformation of the object. The model was optimized for the convergence strategy, so that it had a wider convergence domain and a better convergence effect than either of the original point-to-point or point-to-point constrained models. Secondly, for outliers and overlapping values, the GSAW-ICP algorithm was able to assign appropriate weights, so as to optimize both the noise and outlier interference of the overall system. Our proposed algorithm was extensively tested on noisy, anomalous, and real datasets, and the proposed method was proven to have a better performance than other state-of-the-art algorithms.