Iterative Closest Point Algorithm Research Articles

Normscan: open-source Python software to create average models from CT scans.

Age-matched average 3D models facilitate both surgical planning and intraoperative guidance of cranial birth defects such as craniosynostosis. We aimed to develop an algorithm that accepts any number of CT scans as input and generates highly accurate, average models with minimal user input that are ready for 3D printing and clinical use. Using a compiled database of 'normal' pediatric computed tomography (CT) scans, we report Normscan, an open-source platform built in Python that allows users to generate normative models of CT scans through user-defined landmarks. We use the basion, nasion, and left and right porions as anatomical landmarks for initial correspondence and then register the models using the iterative closest points algorithm before downstream averaging. Normscan is fast and easy to use via our user interface and also creates highly accurate average models of any number of input models. Additionally, it is highly repeatable, with coefficients of variance for the surface area and volume of the average model being less than 3% across ten independent trials. Average models can then be 3D printed and/or visualized in augmented reality. Normscan provides an end-to-end pipeline for the creation of average models of skulls. These models can be used for the generation of databases of specific demographic anatomical models as well as for intraoperative guidance and surgical planning. While Normscan was designed for craniosynostosis repair, due to the modular nature of the algorithm, Normscan has many applications in other areas of surgical planning and research.

Automatic extraction of facial median sagittal plane for patients with asymmetry based on the EDMA alignment algorithm.

We aimed to establish a novel method for automatically constructing three-dimensional (3D) median sagittal plane (MSP) for mandibular deviation patients, which can increase the efficiency of aesthetic evaluating treatment progress. We developed a Euclidean weighted Procrustes analysis (EWPA) algorithm for extracting 3D facial MSP based on the Euclidean distance matrix analysis, automatically assigning weight to facial anatomical landmarks. Forty patients with mandibular deviation were recruited, and the Procrustes analysis (PA) algorithm based on the original mirror alignment and EWPA algorithm developed in this study were used to construct the MSP of each facial model of the patient as experimental groups 1 and 2, respectively. The expert-defined regional iterative closest point algorithm was used to construct the MSP as the reference group. The angle errors of the two experimental groups were compared to those of the reference group to evaluate their clinical suitability. The angle errors of the MSP constructed by the two EWPA and PA algorithms for the 40 patients were 1.39 ± 0.85°, 1.39 ± 0.78°, and 1.91 ± 0.80°, respectively. The two EWPA algorithms performed best in patients with moderate facial asymmetry, and in patients with severe facial asymmetry, the angle error was below 2°, which was a significant improvement over the PA algorithm. The clinical application of the EWPA algorithm based on 3D facial morphological analysis for constructing a 3D facial MSP for patients with mandibular deviated facial asymmetry deformity showed a significant improvement over the conventional PA algorithm and achieved the effect of a dental clinical expert-level diagnostic strategy.

An effective point cloud registration method for three-dimensional reconstruction of pressure piping

Abstract At present, industrial scenes with sparse features and weak textures are widely encountered, and the three-dimensional reconstruction of such scenes is a recognized problem. Pressure pipelines have a wide range of applications in fields such as petroleum engineering, chemical engineering, and hydropower station engineering. However, there is no mature solution for the three-dimensional reconstruction of pressure pipes. The main reason is that the typical scenes in which pressure pipes are found also have relatively few features and textures. Traditional three-dimensional reconstruction algorithms based on feature extraction are largely ineffective for such scenes that are lacking in features. In view of the above problems, this paper proposes an improved interframe registration algorithm based on point cloud fitting with cylinder axis vector constraints. By incorporating geometric feature parameters of a cylindrical pressure pipeline, specifically the axis vector of the cylinder, to constrain the traditional iterative closest point algorithm, the accuracy of point cloud registration can be improved in scenarios lacking features and textures, and some environmental uncertainties can be overcome. Finally, using actual laser point cloud data collected from pressure pipelines, the proposed fitting-based point cloud registration algorithm with cylinder axis vector constraints is tested. The experimental results show that under the same conditions, compared with other open-source point cloud registration algorithms, the proposed method can achieve higher registration accuracy. Moreover, integrating this algorithm into an open-source three-dimensional reconstruction algorithm framework can lead to better reconstruction results.

MF-LIO: integrating multi-feature LiDAR inertial odometry with FPFH loop closure in SLAM

Abstract Simultaneous localization and mapping (SLAM) is a pivotal technology in autonomous vehicle navigation and a significant research area in robotics. Addressing the inaccuracies in point cloud registration of traditional LiDAR SLAM, which lead to localization and mapping errors, we propose a novel LiDAR inertial odometry approach integrating inertial measurement units and a multi-feature joint registration strategy. Initially, we introduce an innovative ground segmentation method and feature categorization strategy, enhancing ground detection mechanisms and optimizing the feature extraction process. Subsequently, our multi-feature joint registration method computes the pose transformations between current frames and the local map. Finally, we employ a global registration method based on fast point feature histograms feature descriptors for coarse alignment, providing initial estimates for the generalized iterative closest point algorithm, thus efficiently and accurately mitigating cumulative errors. Extensive evaluations on the KITTI dataset and real-world campus environment demonstrate that our approach significantly surpasses existing advanced LiDAR SLAM solutions, achieving over a 24% improvement in pose estimation accuracy.

Toward Robotic Nuclear Decommissioning: Deep Learning-Based Object Classification and Pose Estimation from Partial-View Scans

This study introduces a novel framework for the robotic decommissioning of nuclear facilities, that focuses on object classification and six degrees of freedom pose estimation from partial-view three-dimensional (3-D) scan data. Addressing the challenge of precise robotic manipulation in environments where acquiring full-scan data is impractical, this framework leverages a deep neural network for initial pose estimation, subsequently refined by a modified iterative closest point algorithm. Our method demonstrates high accuracy in identifying scanned objects and estimating their poses from partial-view scans, validated through experiments with 3-D printed mock-ups. This advancement highlights the potential for significantly enhancing robotic automation in nuclear decommissioning and related fields.

Research on prediction of water target motion status of unmanned surface vehicle based on lidar

To improve the perception ability of unmanned boats on water targets and improve the autonomous collision avoidance function of unmanned boats, the problem of target motion state prediction is studied and a method for predicting the motion state of unmanned water targets based on lidar is proposed. Firstly, a three-dimensional point cloud space model is established and the down-sampling and clutter filtering are completed through various filters. Secondly, based on European clustering target segmentation, a target tracking algorithm combined with 3D-IOU metric is proposed to achieve target tracking. Finally, the Kalman filter is introduced for denoising, combined with the iterative closest point algorithm (Iterative Closest Point, ICP) and the centroid calculation algorithm to realize the prediction of the target speed and course. After the test on the lake, the method can meet the real-time requirements, complete the accurate prediction of the target motion state and control the error at a low level. At the same time, this method can also lay the foundation for the development of subsequent autonomous navigation tasks of unmanned vehicles.

Research on Deformation Monitoring of Roadway Surrounding Rock Based on Mobile 3D Laser Scanning Technology

Aiming at the problems of low data acquisition efficiency, unintuitive data form, low relative accuracy, and inability to continuously monitor the whole cross-section in traditional roadway rock deformation monitoring methods, a roadway rock deformation monitoring method based on mobile 3D laser scanning technology is proposed. Firstly, the mobile 3D laser scanning technology is used to obtain the real 3D point cloud data of the roadway surrounding rock; secondly, the multi-scale dimensional features are used to classify the 3D point cloud data, filter the invalid point cloud data, and form the accurate point cloud data of the roadway surrounding rock surface; then the 3D laser scanning is carried out on a regular basis for the deformation-prone areas of the roadway, and the Iterative Closest Point Algorithm (ICP algorithm) is used to align the overlapping part of the point cloud to achieve the point cloud splicing and point cloud stitching of the two scans. The point cloud splicing and point cloud overlapping of the two scans are achieved; finally, the multi-scale model-to-model point cloud comparison algorithm (M3C2 algorithm) is used to compare the point cloud data of the two periods of the roadway, and the appropriate parameters are selected to indicate the displacement change of the surface of the roadway rock with the size of the color value, so as to realize the monitoring of the deformation of the roadway rock in the three-dimensional full cross-section of the roadway. The method is applied to the deformation monitoring of the surrounding rock in the return roadway of 21407 working face in a coal mine, and the results show that: the mobile laser scanning technology can be used to obtain the complete point cloud data of the roadway, and the deformation area of the surrounding rock is successfully identified after the classification of the scanned point clouds in different time periods, the alignment of the point clouds, and the comparison of the point clouds, and the deformation condition of the surrounding rock of the roadway is visually reflected in the three-dimensional full-section deformation condition of the surrounding rock of the roadway. This method is suitable for monitoring the overall de-formation of the surrounding rock of the roadway under complex environmental conditions, and the de-formation of the surrounding rock is identified by different color areas.

Regression Method for Rail Fastener Tightness Based on Center-Line Projection Distance Feature and Neural Network

In the railway system, fasteners have the functions of damping, maintaining the track distance, and adjusting the track level. Therefore, routine maintenance and inspection of fasteners are important to ensure the safe operation of track lines. Currently, assessment methods for fastener tightness include manual observation, acoustic wave detection, and image detection. There are limitations such as low accuracy and efficiency, easy interference and misjudgment, and a lack of accurate, stable, and fast detection methods. Aiming at the small deformation characteristics and large elastic change of fasteners from full loosening to full tightening, this study proposes high-precision surface-structured light technology for fastener detection and fastener deformation feature extraction based on the center-line projection distance and a fastener tightness regression method based on neural networks. First, the method uses a 3D camera to obtain a fastener point cloud and then segments the elastic rod area based on the iterative closest point algorithm registration. Principal component analysis is used to calculate the normal vector of the segmented elastic rod surface and extract the point on the centerline of the elastic rod. The point is projected onto the upper surface of the bolt to calculate the projection distance. Subsequently, the mapping relationship between the projection distance sequence and fastener tightness is established, and the influence of each parameter on the fastener tightness prediction is analyzed. Finally, by setting up a fastener detection scene in the track experimental base, collecting data, and completing the algorithm verification, the results showed that the deviation between the fastener tightness regression value obtained after the algorithm processing and the actual measured value RMSE was 0.2196 mm, which significantly improved the effect compared with other tightness detection methods, and realized an effective fastener tightness regression.

Does facial asymmetry vary between subjects of different age groups? A 3D stereophotogrammetry analysis

This study was aimed to assess whether facial asymmetry increases with age and to examine potential gender differences using 3D stereophotogrammetry. A prospective cross-sectional study was performed. 3D photographs were acquired from 600 control subjects, 300 male, 300 female, and were stratified into 15 different age groups ranging from 0 to 70+. The 3D photographs were postprocessed and mirrored. The original and mirrored faces were surface-based matched using an iterative closest point algorithm. The primary outcome variable, facial asymmetry, was evaluated by calculating the absolute mean distance between the original and mirrored images. The primary predictor was age. Pearson’s correlation was used to assess the correlation between facial asymmetry and age. The average overall facial asymmetry was 0.72 mm (SD 0.72 mm; range 0.25 – 3.04 mm). Mean facial asymmetry increased significantly with age, from 0.45 mm in the age group of 0-4 years to 0.98 mm in the age group of 70+ (p<0.001). Facial asymmetry was positively correlated with age (Pearson’s r = 0.55; p<0.001). Male subjects were significantly more asymmetric compared to females, 0.77 mm and 0.67 mm, respectively (p<0.001). This study indicates that facial asymmetry significantly increases with age and is significantly larger in males than in females.

Experimental research based on robot-assisted surgery: Lower limb fracture reduction surgery planning navigation system.

Lower extremity fracture reduction surgery is a key step in the treatment of lower extremity fractures. How to ensure high precision of fracture reduction while reducing secondary trauma during reduction is a difficult problem in current surgery. First, segmentation and three-dimensional reconstruction are performed based on fracture computed tomography images. A cross-sectional point cloud extraction algorithm based on the normal filtering of the long axis of the bone is designed to obtain the cross-sectional point clouds of the distal bone and the proximal bone, and the optimal reset target pose of the broken bone is obtained by using the iterative closest point algorithm. Then, the optimal reset sequence of reset parameters was determined, combined with the broken bone collision detection algorithm, a surgical planning algorithm for lower limb fracture reset was proposed, which can effectively reduce the reset force while ensuring the accuracy of the reset process without collision. The average error of the reduction of the model bone was within 1.0 mm. The reduction operation using the planning and navigation system of lower extremity fracture reduction surgery can effectively reduce the reduction force. At the same time, it can better ensure the smooth change of the reduction force. Planning and navigation system of lower extremity fracture reduction surgery is feasible and effective.

Improving ultrasound-based bone registration using the iterative closest point algorithm paired with a complex optimization solver

The Iterative Close Point (ICP) algorithm is used for bone registrations based on ultrasound measurements. However, the ICP has been shown to suffer from local minima. The Complex optimization, as a more robust routine compared to the commonly used gradient-based algorithms, could be an alternative for solving the ICP problem. In this study, we investigated the effect of the initial estimate and the number of registration points on bone registrations achieved using the ICP and a Complex optimization routine and we compared it against using Quadratic Sequential Programming (SQP). Ultrasound measurements were performed with an A-mode probe on a bovine humerus and an ovine femur embedded into ballistic gel. Simultaneously, the bones and the probe were tracked in 3D space using retroreflective markers. Kinematic, ultrasound and geometrical data obtained from scans of the specimens and the probe served as input to a bone registrations routine. Registrations were performed using two ICP solvers for different initial estimates and number of registration points. On average, 68 % of the Complex optimization registrations had less than 1 mm translation error and less than 1° rotational error for perturbations of the initial estimate from the reference measurements compared to the 35 % of the SQP ones. Similar medians of registration errors were observed between the two methods for variations of the number of the employed registration points. Although the Complex optimization provided accurate bone registrations for all cases, the objective function could not always determine the registrations with the smallest registration error. Future research should explore methodologies to overcome this challenge.

Digital Twin and Deep Reinforcement Learning-Driven Robotic Automation System for Confined Workspaces: A Nozzle Dam Replacement Case Study in Nuclear Power Plants

Robotic automation has emerged as a leading solution for replacing human workers in dirty, dangerous, and demanding industries to ensure the safety of human workers. However, practical implementation of this technology remains limited, requiring substantial effort and costs. This study addresses the challenges specific to nuclear power plants, characterized by hazardous environments and physically demanding tasks such as nozzle dam replacement in confined workspaces. We propose a digital twin and deep-reinforcement-learning-driven robotic automation system with an autonomous mobile manipulator. The study follows a four-step process. First, we establish a simplified testbed for a nozzle dam replacement task and implement a high-fidelity digital twin model of the real-world testbed. Second, we employ a hybrid visual perception system that combines deep object pose estimation and an iterative closest point algorithm to enhance the accuracy of the six-dimensional pose estimation. Third, we use a deep-reinforcement-learning method, particularly the proximal policy optimization algorithm with inverse reachability map, and a centroidal waypoint strategy, to improve the controllability of an autonomous mobile manipulator. Finally, we conduct pre-performed simulations of the nozzle dam replacement in the digital twin and evaluate the system on a robot in the real-world testbed. The nozzle dam replacement with precise object pose estimation, navigation, target object grasping, and collision-free motion generation was successful. The robotic automation system achieved a 92.0%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$92.0\\%$$\\end{document} success rate in the digital twin. Our proposed method can improve the efficiency and reliability of robotic automation systems for extreme workspaces and other perilous environments.

A Novel Multimodal Fusion Framework Based on Point Cloud Registration for Near-Field 3D SAR Perception

This study introduces a pioneering multimodal fusion framework to enhance near-field 3D Synthetic Aperture Radar (SAR) imaging, crucial for applications like radar cross-section measurement and concealed object detection. Traditional near-field 3D SAR imaging struggles with issues like target–background confusion due to clutter and multipath interference, shape distortion from high sidelobes, and lack of color and texture information, all of which impede effective target recognition and scattering diagnosis. The proposed approach presents the first known application of multimodal fusion in near-field 3D SAR imaging, integrating LiDAR and optical camera data to overcome its inherent limitations. The framework comprises data preprocessing, point cloud registration, and data fusion, where registration between multi-sensor data is the core of effective integration. Recognizing the inadequacy of traditional registration methods in handling varying data formats, noise, and resolution differences, particularly between near-field 3D SAR and other sensors, this work introduces a novel three-stage registration process to effectively address these challenges. First, the approach designs a structure–intensity-constrained centroid distance detector, enabling key point extraction that reduces heterogeneity and accelerates the process. Second, a sample consensus initial alignment algorithm with SHOT features and geometric relationship constraints is proposed for enhanced coarse registration. Finally, the fine registration phase employs adaptive thresholding in the iterative closest point algorithm for precise and efficient data alignment. Both visual and quantitative analyses of measured data demonstrate the effectiveness of our method. The experimental results show significant improvements in registration accuracy and efficiency, laying the groundwork for future multimodal fusion advancements in near-field 3D SAR imaging.

INTRAOPERATIVE SURGICAL NAVIGATION BASED ON LASER SCANNER FOR IMAGE-GUIDED ORAL AND MAXILLOFACIAL SURGERY

In oral and maxillofacial surgery, computer-assisted navigation technologies have been widely used to achieve intraoperative positioning. The traditional methods mainly rely on the experience of doctors and the difference between the locations of key points in the surgical area and the preoperative planning, which have certain limitations. In this paper, a new intraoperative surgical navigation framework based on mobile laser scanner is proposed, which ensures that surgery is performed accurately according to the preoperative planning. The framework mainly includes two parts. First, the real-time surface reconstruction of the anatomy should be realized during the operation. Second, the acquired image is matched to the planned image in real time. Although the most common method of surface reconstruction is to render the volume directly from raw data or render the surface from the segmented data using computed tomography/magnetic resonance (CT/MR) data, this method is too complicated for performing the real-time operation during surgery. Furthermore, a new surface registration technique is proposed for image-guided oral and maxillofacial surgery based on the point sets. To improve the registration accuracy and robustness, the point sets are modeled by Mixed Student’s t-Distribution model. In the experiments, the point sets of CT data are from 10 patients with craniomaxillofacial diseases and the surface point set is from the LRS. The TRE of 10 data was less than 1[Formula: see text]mm. Compared with the paired-point registration method and Iterative Closest Point algorithm, the results demonstrated better performance of the proposed method, the surgical situation can be displayed in real time during the surgical process, and any differences from the surgical plan can also be reflected.

Motion prediction of tumbling uncooperative spacecraft during proximity operations.

The relative attitude estimation between chasers and uncooperative targets is an important prerequisite for executing in orbit service (OOS) tasks. Only by efficiently obtaining relative pose parameters can chasers design close-range rendezvous trajectories close to uncooperative targets. The focus of this article is on active systems, such as TOF cameras or LIDAR. This paper proposes an attitude estimation scheme to obtain relative attitude parameters between uncooperative targets. This scheme utilizes LIDAR to obtain three-dimensional point clouds of non-cooperative targets, extracts key points and simplifies the number of point clouds through joint farthest point sampling and point cloud feature analysis, and then uses point fast feature histograms (FPFHs) and robust iterative closest point algorithms to achieve point cloud registration between every two frames. Finally, a filtering framework was designed, whose scheme is an extended Kalman filter designed for updating measurements of relative position, velocity, attitude, and angular velocity estimation. The experimental results show that this method can effectively achieve point cloud registration for close range rotation and translation motion, and can estimate the motion state of the target.

An improved iterative closest point algorithm based on the particle filter and K-means clustering for fine model matching

An improved iterative closest point algorithm based on the particle filter and K-means clustering for fine model matching

FMCW Radar on LiDAR map localization in structural urban environments

AbstractMultisensor fusion‐based localization technology has achieved high accuracy in autonomous systems. How to improve the robustness is the main challenge at present. The most commonly used LiDAR and camera are weather‐sensitive, while the frequency‐modulated continuous wave Radar has strong adaptability but suffers from noise and ghost effects. In this paper, we propose a heterogeneous localization method called Radar on LiDAR Map, which aims to enhance localization accuracy without relying on loop closures by mitigating the accumulated error in Radar odometry in real time. To accomplish this, we utilize LiDAR scans and ground truth paths as Teach paths and Radar scans as the trajectories to be estimated, referred to as Repeat paths. By establishing a correlation between the Radar and LiDAR scan data, we can enhance the accuracy of Radar odometry estimation. Our approach involves embedding the data from both Radar and LiDAR sensors into a density map. We calculate the spatial vector similarity with an offset to determine the corresponding place index within the candidate map and estimate the rotation and translation. To refine the alignment, we utilize the Iterative Closest Point algorithm to achieve optimal matching on the LiDAR submap. The estimated bias is subsequently incorporated into the Radar SLAM for optimizing the position map. We conducted extensive experiments on the Mulran Radar Data set, Oxford Radar RobotCar Dataset, and our data set to demonstrate the feasibility and effectiveness of our proposed approach. Our proposed scan projection descriptors achieves homogeneous and heterogeneous place recognition and works much better than existing methods. Its application to the Radar SLAM system also substantially improves the positioning accuracy. All sequences' root mean square error is 2.53 m for positioning and 1.83° for angle.

Registration of preoperative temporal bone CT-scan to otoendoscopic video for augmented-reality based on convolutional neural networks.

Patient-to-image registration is a preliminary step required in surgical navigation based on preoperative images. Human intervention and fiducial markers hamper this task as they are time-consuming and introduce potential errors. We aimed to develop a fully automatic 2D registration system for augmented reality in ear surgery. CT-scans and corresponding oto-endoscopic videos were collected from 41 patients (58 ears) undergoing ear examination (vestibular schwannoma before surgery, profound hearing loss requiring cochlear implant, suspicion of perilymphatic fistula, contralateral ears in cases of unilateral chronic otitis media). Two to four images were selected from each case. For the training phase, data from patients (75% of the dataset) and 11 cadaveric specimens were used. Tympanic membranes and malleus handles were contoured on both video images and CT-scans by expert surgeons. The algorithm used a U-Net network for detecting the contours of the tympanic membrane and the malleus on both preoperative CT-scans and endoscopic video frames. Then, contours were processed and registered through an iterative closest point algorithm. Validation was performed on 4 cases and testing on 6 cases. Registration error was measured by overlaying both images and measuring the average and Hausdorff distances. The proposed registration method yielded a precision compatible with ear surgery with a 2D mean overlay error of mm for the incus and mm for the round window. The average Hausdorff distance for these 2 targets was mm and mm respectively. An outlier case with higher errors (2.3 mm and 1.5 mm average Hausdorff distance for incus and round window respectively) was observed in relation to a high discrepancy between the projection angle of the reconstructed CT-scan and the video image. The maximum duration for the overall process was 18s. A fully automatic 2D registration method based on a convolutional neural network and applied to ear surgery was developed. The method did not rely on any external fiducial markers nor human intervention for landmark recognition. The method was fast and its precision was compatible with ear surgery.

Technique on Vehicle Damage Assessment After Collisions Using Optical Radar Technology and Iterative Closest Point Algorithm

Technique on Vehicle Damage Assessment After Collisions Using Optical Radar Technology and Iterative Closest Point Algorithm

A Deformation Field-Based Approach for Expression Processing in Head Motion Correction

It is a routine procedure to use Iterative Closest Point (ICP) algorithm to correct motion in medical imaging of the head. However, the basic assumption of ICP is the rigid body motion, and it is not strictly valid when there is non-rigid motion (facial expression). In this study, we propose a novel method to reduce the adverse effects of facial expressions on head motion estimation. First, we design a network named DFG-Net to generate a deformation field on the RGB domain and compute a confidence map. We then extend the original ICP by embedding the confidence map representing each point’s degree of non-rigid motion. Compared to other techniques, our proposed method eliminates the negative effects of facial expression while making the most of pose information. The experiments demonstrate that our approach reduces the position error by 19.23% and orientation error by 36.37% compared to the original ICP algorithm. They also show that our method is robust to variations of expressions.