Position Accuracy Research Articles

Optimization of polarization balance in beam splitter films for weak star simulator

In the weak star simulator, the background stray light of the self-excited star image and the simulated target star light are mixed with each other, which is difficult to separate and reduces the simulation accuracy of the star position. Therefore, based on the polarized light tracing method, this paper explores the induction mechanism of the polarization effect of the weak star simulator on the background stray light field of the star map, and proposes a polarization balance optimization method for the beam splitter film. At the same time, the mapping model of star position simulation accuracy, polarization parameters of beam splitter and polarization stray light suppression degree is established. Based on this evaluation function, the polarization balance optimization process of beam splitter is constructed to reduce the influence of polarization effect of beam splitter on the background stray light field of star image. The simulation and experimental results show that the stray light suppression ability of the weak star simulator is improved by 2.1 times and the simulation accuracy of the star position is improved by 1.64 times after the polarization balance optimization of the beam splitter film.

Evaluation of the resection plane three-dimensional positional accuracy using a resection guide directional guidance slot; a randomized clinical trial

AimThe study was performed to compare the mandibular resection guide with a directional guidance slot with the conventional guide regarding three-dimensional positional accuracy.Materials and methodsTwenty-six patients with lateral segmental mandibular defects were selected, and randomly allocated into two groups. All defects were managed with preoperative virtual surgical planning. Resection in the test group was conducted using a resection guide with a directional guidance slot, while a conventional resection guide design was utilized in the control group. The linear and angular deviation of the osteotomy planes was analyzed for both groups, along with the accuracy of the insertion of the reconstruction bone block in the resected defect. Data were documented, absolute deviation was calculated, statistical analysis was performed and significance was set at the 5% level.ResultsThe cases conducted with a directional guidance templet reported a statistically significant difference when compared to the conventional edge-cutting guide regarding the linear and angular spatial osteotomy plane position (P < 0.001). The defect span analysis reported excellent levels of agreement in both groups (ICC = 1.00, ICC = 0.995), however, the difference between the groups was statistically significant (P < 0.001).ConclusionThe study demonstrated the enhanced positional accuracy of the resection plane and reconstruction block placement when a directional slot is incorporated in the computer-generated resection guide.

TAAM refinement on high-resolution experimental and simulated 3D ED/MicroED data for organic molecules.

3D electron diffraction (3D ED), or microcrystal electron diffraction (MicroED), has become an alternative technique for determining the high-resolution crystal structures of compounds from sub-micron-sized crystals. Here, we considered L-alanine, α-glycine and urea, which are known to form good-quality crystals, and collected high-resolution 3D ED data on our in-house TEM instrument. In this study, we present a comparison of independent atom model (IAM) and transferable aspherical atom model (TAAM) kinematical refinement against experimental and simulated data. TAAM refinement on both experimental and simulated data clearly improves the model fitting statistics (R factors and residual electrostatic potential) compared to IAM refinement. This shows that TAAM better represents the experimental electrostatic potential of organic crystals than IAM. Furthermore, we compared the geometrical parameters and atomic displacement parameters (ADPs) resulting from the experimental refinements with the simulated refinements, with the periodic density functional theory (DFT) calculations and with published X-ray and neutron crystal structures. The TAAM refinements on the 3D ED data did not improve the accuracy of the bond lengths between the non-H atoms. The experimental 3D ED data provided more accurate H-atom positions than the IAM refinements on the X-ray diffraction data. The IAM refinements against 3D ED data had a tendency to lead to slightly longer X-H bond lengths than TAAM, but the difference was statistically insignificant. Atomic displacement parameters were too large by tens of percent for L-alanine and α-glycine. Most probably, other unmodelled effects were causing this behaviour, such as radiation damage or dynamical scattering.

Fast and Reliable Network RTK Positioning Based on Multi-Frequency Sequential Ambiguity Resolution under Significant Atmospheric Biases

The positioning performance of the Global Navigation Satellite System (GNSS) network real-time kinematic (NRTK) depends on regional atmospheric error modeling. Under normal atmospheric conditions, NRTK positioning provides high accuracy and rapid initialization. However, fluctuations in atmospheric conditions can lead to poor atmospheric error modeling, resulting in significant atmospheric biases that affect the positioning accuracy, initialization speed, and reliability of NRTK positioning. Consequently, this decreases the efficiency of NRTK operations. In response to these challenges, this paper proposes a fast and reliable NRTK positioning method based on sequential ambiguity resolution (SAR) of multi-frequency combined observations. This method processes observations from extra-wide-lane (EWL), wide-lane (WL), and narrow-lane (NL) measurements; performs sequential AR using the LAMBDA algorithm; and subsequently constrains other parameters using fixed ambiguities. Ultimately, this method achieves high precision, rapid initialization, and reliable positioning. Experimental analysis was conducted using Continuous Operating Reference Station (CORS) data, with baseline lengths ranging from 88 km to 110 km. The results showed that the proposed algorithm offers positioning accuracy comparable to conventional algorithms in conventional NRTK positioning and has higher fixed rate and positioning accuracy in single-epoch positioning. On two datasets, the proposed algorithm demonstrated over 30% improvement in time to first fix (TTFF) compared to conventional algorithms. It provides higher precision in suboptimal positioning solutions when conventional NRTK algorithms fail to achieve fixed solutions during the initialization phase. These experiments highlight the advantages of the proposed algorithm in terms of initialization speed and positioning reliability.

Vision-Based UAV Detection and Localization to Indoor Positioning System.

In recent years, the technological landscape has undergone a profound metamorphosis catalyzed by the widespread integration of drones across diverse sectors. Essential to the drone manufacturing process is comprehensive testing, typically conducted in controlled laboratory settings to uphold safety and privacy standards. However, a formidable challenge emerges due to the inherent limitations of GPS signals within indoor environments, posing a threat to the accuracy of drone positioning. This limitation not only jeopardizes testing validity but also introduces instability and inaccuracies, compromising the assessment of drone performance. Given the pivotal role of precise GPS-derived data in drone autopilots, addressing this indoor-based GPS constraint is imperative to ensure the reliability and resilience of unmanned aerial vehicles (UAVs). This paper delves into the implementation of an Indoor Positioning System (IPS) leveraging computer vision. The proposed system endeavors to detect and localize UAVs within indoor environments through an enhanced vision-based triangulation approach. A comparative analysis with alternative positioning methodologies is undertaken to ascertain the efficacy of the proposed system. The results obtained showcase the efficiency and precision of the designed system in detecting and localizing various types of UAVs, underscoring its potential to advance the field of indoor drone navigation and testing.

Superficial Defect Detection for Concrete Bridges Using YOLOv8 with Attention Mechanism and Deformation Convolution

The accuracy of detecting superficial bridge defects using the deep neural network approach decreases significantly under light variation and weak texture conditions. To address these issues, an enhanced intelligent detection method based on the YOLOv8 deep neural network is proposed in this study. Firstly, multi-branch coordinate attention (MBCA) is proposed to improve the accuracy of coordinate positioning by introducing a global perception module in coordinate attention mechanism. Furthermore, a deformable convolution based on MBCA is developed to improve the adaptability for complex feature shapes. Lastly, the deformable convolutional network attention YOLO (DCNA-YOLO) detection algorithm is formed by replacing the deep C2F structure in the YOLOv8 architecture with a deformable convolution. A supervised dataset consisting of 4794 bridge surface damage images is employed to verify the proposed method, and the results show that it achieves improvements of 2.0% and 3.4% in mAP and R. Meanwhile, the model complexity decreases by 1.2G, increasing the detection speed by 3.5/f·s−1.

Evaluating the accuracy of one-piece and three-piece 3D-printed indirect bonding transfer trays: An in vitro study.

Recent developments in computer-aided design/computer-aided manufacturing (CAD/CAM) and 3D printing have enabled the fabrication of digital indirect bonding (IDB) transfer trays. These modern products require thorough investigation. The aim of the study was to determine the accuracy of one-piece and three-piece IDB transfer trays in vitro. An initial dental scan (IDS) of a randomly selected patient with digitally positioned brackets served as the master scan (MS) for designing 16 IDB transfer trays of each type. They were 3D printed and used for bonding 448 brackets to the models. Subsequently, the models were scanned with a TRIOS® 3 Intraoral Scanner (3Shape A/S, Copenhagen, Denmark), producing actual scans (ASs). The accuracy of bracket positioning was measured digitally on both MSs and ASs. The measurements were compared to the Objective Grading System for dental casts provided by the American Board of Orthodontics (ABO). The 2 types of IDB transfer trays showed comparable accuracy. All linear errors were within the clinically acceptable range, whereas the angular measurements demonstrated significant variability, resulting in clinically unacceptable transfer errors that ranged from 3.3% to 90.3%. The study results cannot be unconditionally extrapolated to other types of IDB transfer trays due to the diversity of their properties and features. The study evaluated the in vitro accuracy of IDB transfer trays. The revealed number of errors may be even higher in vivo due to limitations in visibility, salivary flow, interference from the tongue, and difficulties in achieving a proper fit of the IDB transfer tray to the teeth.

Multi-mode vehicle pose estimation under different GNSS conditions

The integrated navigation system combining the global navigation satellite system (GNSS) and inertial navigation system (INS) is a crucial method for pose estimation in the field of autonomous driving technologies. Nevertheless, the accuracy of pose estimation is severely compromised when GNSS signals are obstructed or disrupted. To address this issue, this study introduces a multi-mode pose estimation framework designed to ensure accurate pose estimation even under unstable GNSS conditions. By integrating vehicle kinematics model that considers steering characteristics (VKMSC) and the convolutional neural network-long short-term memory (CNN-LSTM) neural network (NN) model into various estimation modes, the framework enhances the robustness of the integrated navigation system against signal interference. The system dynamically selects the optimal estimation strategy based on the degree of GNSS signal disruption. The proposed method has been validated through real-vehicle experiments, which demonstrate its efficacy in providing precise pose estimation across a spectrum of interference scenarios. Under the multipath and non-line-of-sight (MP/NLOS) mode, compared to the integrated navigation system and the fusion of traditional vehicle kinematic models, the proposed method improved positional estimation accuracy by 61.8 % and 19.7 %, respectively. In GNSS outage mode, the proposed method increased the estimation accuracy by 36.5 % and 12.0 %, respectively, compared to the INS navigation system assisted by the VKMSC and CNN-LSTM network model. The proposed method effectively reduces pose estimation errors in the integrated navigation system during interference and suppresses data fluctuations, thereby enhancing the system's precision and robustness.

Remote Sensing Scene Classification using ConvNeXt-Tiny Model with Attention Mechanism and Label Smoothing

Remote Sensing Scene Classification (RSSC) is the discrete categorization of remote sensing images into various classes of scene categories based on their image content. RSSC plays an important role in many fields, such as agriculture, land mapping, and identification of disaster-prone areas. Therefore, a reliable and accurate RSSC algorithm is required to ensure the accuracy of land identification. Many existing studies in recent years have used deep learning methods, especially CNN combined with attention modules to solve this problem. This study focuses on solving the RSSC problem by proposing a deep learning-based method (CNN) with the ConvNeXt-Tiny model integrated with Efficient Channel Attention Module (ECANet) and label smoothing regularization (LSR). The ConvNeXt-Tiny model shows that a persistent superior outperforms the ‘large’ model in convinced metrics. The ConvNeXt-Tiny model also has a huge advantage in high-precision positioning and higher classification accuracy and localization precision in a variety of complicated scenarios of remote sensing scene recognition. The experiments in this study also aim to prove that the integration of the attention module and LSR into the basic CNN network can improve precision, because the attention module can strengthen important features and weaken features that are less useful for classification. The experimental results proved that the integration of ECANet and LSR in the ConvNeXt-Tiny base network obtained a higher precision of 0.38% in the UC-Merced dataset, 0.7% in the AID, and 0.4% in the WHU-RS19 dataset than the ConvNeXt-Tiny model without ECANet and LSR. The ConvNeXt-Tiny model with ECANet integration and LSR obtained an accuracy of 99.00±0.41% in the UC-Merced dataset, 95.08±0.20% in AID, and 99.50±0.31% in the WHU-RS19 dataset.

Voronoi Diagram-based USBL Outlier Rejection for AUV Localization

USBL systems are essential for providing accurate positions of autonomous underwater vehicles (AUVs). On the other hand, the accuracy can be degraded by outliers because of the environmental conditions. A failure to address these outliers can significantly impact the reliability of underwater localization and navigation systems. This paper proposes a novel outlier rejection algorithm for AUV localization using Voronoi diagrams and query point calculation. The Voronoi diagram divides data space into Voronoi cells that center on ultra-short baseline (USBL) data, and the calculated query point determines if the corresponding USBL data is an inlier. This study conducted experiments acquiring GPS and USBL data simultaneously and optimized the algorithm empirically based on the acquired data. In addition, the proposed method was applied to a sensor fusion algorithm to verify its effectiveness, resulting in improved pose estimations. The proposed method can be applied to various sensor fusion algorithms as a preprocess and could be used for outlier rejection for other 2D-based location sensors.

From plan to delivery: Machine learning based positional accuracy prediction of multi-leaf collimator and estimation of delivery effect in volumetric modulated arc therapy.

The positional accuracy of MLC is an important element in establishing the exact dosimetry in VMAT. We comprehensively analyzed factors that may affect MLC positional accuracy in VMAT, and constructed a model to predict MLC positional deviation and estimate planning delivery quality according to the VMAT plans before delivery. A total of 744 "dynalog" files for 23 VMAT plans were extracted randomly from treatment database. Multi-correlation was used to analyzed the potential influences on MLC positional accuracy, including the spatial characteristics and temporal variability of VMAT fluence, and the mechanical wear parameters of MLC. We developed a model to forecast the accuracy of MLC moving position utilizing the random forest (RF) ensemble learning method. Spearman correlation was used to further investigate the associations between MLC positional deviation and dosage deviations as well as gamma passing rates. The MLC positional deviation and effective impact factors show a strong multi-correlation (R=0.701, p-value<0.05). This leads to the development of a highly accurate prediction model with average variables explained of 95.03% and average MSE of 0.059 in the 5-fold cross-validation, and MSE of 0.074 for the test data was obtained. The absolute dose deviations caused by MLC positional deviation ranging from 12.948 to 210.235cGy, while the relative volume deviation remained small at 0.470%-5.161%. The average MLC positional deviation correlated substantially with gamma passing rates (with correlation coefficient of -0.506 to -0.720 and p-value<0.05) but marginally with dosage deviations (with correlation coefficient<0.498 and p-value>0.05). The RF predictive model provides a prior tool for VMAT quality assurance.

Three-Dimensional Dead-Reckoning Based on Lie Theory for Overcoming Approximation Errors

This paper proposes a dead-reckoning (DR) method for vehicles using Lie theory. This approach treats the pose (position and attitude) and velocity of the vehicle as elements of the Lie group SE2(3) and follows the computations based on Lie theory. Previously employed DR methods, which have been widely used, suffer from cumulative errors over time due to inaccuracies in the calculated changes from velocity during the motion of the vehicle or small errors in modeling assumptions. Consequently, this results in significant discrepancies between the estimated and actual positions over time. However, by treating the pose and velocity of the vehicle as elements of the Lie group, the proposed method allows for accurate solutions without the errors introduced by linearization. The incremental updates for pose and velocity in the DR computation are represented in the Lie algebra. Experimental results confirm that the proposed method improves the accuracy of DR. In particular, as the motion prediction time interval of the vehicle increases, the proposed method demonstrates a more pronounced improvement in positional accuracy.

Long-term analysis of Sentinel-6A orbit determination: Insights from three years of flight data

The Sentinel-6A mission extends the set of satellites dedicated to continuous ocean altimetry measurements, which started with TOPEX/Poseidon in 1992. To utilize these measurements, high-accuracy orbit solutions with radial position errors of less than 1.5cm (RMS) are required. For precise orbit determination (POD), a dedicated GPS/Galileo triple-frequency receiver (PODRIX) is available. Complementary to this, the TriG receiver provides GPS-only observations for POD and radio occultations. Previous research has shown that reduced-dynamic orbit solutions with PODRIX GPS/Galileo measurements meet the mission requirement for radial position accuracy of 1.5 cm (RMS). However, baseline estimation between the PODRIX and TriG antennas still reveals obvious inconsistencies in the along- and cross-track directions. In this study, we present a comprehensive reprocessing of orbit solutions using three years of Sentinel-6A flight data from both receivers covering January 2021 to December 2023. Cross-comparison between both receivers shows that inconsistencies can be removed by applying a yaw bias and a timing error correction. A comparative analysis of macro-models demonstrates strong correlation of cross-track empirical accelerations with the Sun elevation above the orbital plane when using nominal surface properties reported by the manufacturer. Adjusted macro-models improve performance at the expense of requiring increased surface areas or reflectivity coefficients. In addition to improved radiation pressure models, newly estimated antenna patterns and a time-varying gravity field were used in the analysis. The reprocessed data confirm good agreement of the results of both receivers except for a relative timing error of 1.3 μs, and the yaw bias correction of −0.43° removes previously-observed systematic differences in the estimated empirical accelerations. Satellite laser ranging results based on SLRF2020 station coordinates demonstrate a 5—6 mm (1D RMS) accuracy of the GNSS-based POD solutions across the IGb14/IGS20 reference frame transition in November 2022.

GSE-YOLOv5s: A lightweight visual detection method for first wall tile and bolts (Holes) in CFETR

The detection of the first wall tile and bolts (holes) of CFETR is crucial for improving the efficiency of remote operation maintenance. The internal environment of CFETR is complex, and the existing target detection algorithms lack precision, are prone to missed detection, and occupy substantial computational resources. We introduce a method for detecting tiles and bolts (holes) using a lightweight network. First, a preliminary research platform is established to build a dataset. Secondly, based on YOLOv5s, the GhostBottleNeck module replaces the BottleNeck in the C3 module to build a C3Ghost structure, reducing model computation and parameters. A fast spatial pooling pyramid structure, SimSPPF module, is proposed to replace the SPPF module, increasing model speed. The replacement of the CIOU loss function with EIOU enhances positional accuracy. Comparative experimental results indicate this network outperforms baseline YOLOv5s by increasing the mAP by 1.4 %, decreasing parameters by 13.8 %, and reducing floating-point operations (FLOPS) by 13.2 %. This study holds considerable reference value for future remote visual maintenance in CFETR.

Introducing the double validation metric for radar sensor models

In automated vehicles, environment perception is performed by various sensor types, such as cameras, radars, lidars, and ultrasonics. Simulation models of these sensors, as required in virtual validation methods, are available in various degrees of detail. However, proving the validity of such models is a subject of research. New metrics and methods for credibility assessment of simulation are needed to standardize the validation process in the future. The so-called double validation metric (DVM) has shown advantages and allows an intuitive interpretability of the validation results. The DVM has so far only been applied to lidar sensor models. In this paper, an extension to the DVM is introduced, which is called the DVM Map. A static measurement scenario is conducted in reality and transferred into simulation. The novel method is demonstrated on the obtained real and simulated radar sensor data. In this simple scenario special focus is put on the position accuracy of GNSS reference sensors. Therefore, their impact on the result of sensor model validation is discussed. The paper shows that the method provides a more detailed and accurate validation in comparison to the state of the art of a radar simulation, revealing previously undetected simulation errors. Errors due to the environment model, signal propagation, and signal processing are separated and satellite imagery is used for intuitive visualization of the results. This method is a complementary tool to existing validation techniques to improve the interpretability and judging the trustworthiness of radar simulations.

Method for improving the positional accuracy in passive optical detection of space objects

The number of space objects, i.e., active satellites as well as space debris, is growing at an exponential rate. The management of congested space requires networks of sensors to detect and precisely measure the positions of objects in space. We describe a method that improves the positional accuracy of passive optical detection of space objects. The method is based on a laboratory sensor calibration, which is suitable for all types of cameras (CCD and CMOS cameras) and can easily be implemented for existing staring or tracking stations or even used with space-based passive optical sensors. The successful implementation of the method is demonstrated in a test campaign by measuring tracklets of several Earth-orbiting satellites with a ground-based staring system known as APPARILLO. In addition to its use in space object detection, we encourage that the presented method might also inspire other fields of research and technology, where images need to be acquired with precise timing.

Design and Implementation of Intelligent Robot Control System Integrating Computer Vision and Mechanical Engineering

With the rapid development of science and technology and industry, robot technology has become a key pillar in many fields, especially in industrial manufacturing, robots have been widely used in automated operations. However, in the face of the complex and changeable demands of modern industry, simple mechanized operation can no longer be satisfied, and intelligence has become the inevitable trend of robot technology development. By combining computer vision technology, this study provides robots with powerful environmental awareness and target recognition capabilities, thus achieving higher-level autonomous operation and decision-making. Aiming at the intelligent sorting system, this research adopts XYZ mechanical arm as the execution platform, equipped with TCS34725 color sensor to identify the color of articles, and integrates Hikvision industrial camera and NVIDIA Jetson TX2 image processor to capture and process image data, so as to achieve accurate positioning of articles. In the aspect of software design, this research realizes the function of accurately sorting different color items from the conveyor belt by combining color recognition algorithm and image processing technology with mechanical engineering control. The test results of the system show that the system has high accuracy and stability in color recognition and object positioning, and the trajectory accuracy of the manipulator meets the requirements, which verifies the realization of the research goal. The intelligent robot control system designed in this paper not only improves the level of industrial automation and reduces labor costs, but also broadens the application fields of robots in complex environments.

New line list for the ν4 bands of the trans (790.117 cm–1) and cis (851.943 cm–1) conformers of nitrous acid (HONO): Accurate positions and absolute intensities

The goal of this work was to update and significantly improve the line lists that have been generated recently for 11 µm bands of the trans- and cis- conformer forms of nitrous acid (HONO) [Armante R, Perrin A, Kwabia Tchana F, Manceron L. The ν4 bands at 11 μm: linelists for the trans- and cis- conformer forms of nitrous acid (HONO) in the 2019 version of the GEISA database. Molecular Physics 2021;120:e1951860]. That 2019 version of the 11 µm line list was generated using the spectroscopic parameters that were available, at that time, in the literature. During the present study, we used high-resolution Fourier transform spectra recorded at 11 µm to perform a large investigation of line positions and intensities for the ν4 bands of the trans- and cis-conformer of HONO. The resulting set of experimental ν4 (absolute) intensities and of 41 energy levels were used to determine, by least squares fit computations, improved position and intensity parameters for the ν4 bands of the trans- and cis-conformer of HONO. For trans-HONO, the ν4 band appeared not to be perturbed, while for cis-HONO a weak high order B-type Coriolis, coupling together the 41 and 61 energy levels was evidenced for the first time. This new list is of potential interest for the IASI-NG (Infrared Atmospheric Sounding Interferometer - New Generation) instrument which will be launched on board the METOP-SG satellite in 2025.

Reduced Kinematic Error for Position Accuracy in a High-Torque, Lightweight Actuator

In this paper, we propose the design, development, and testing of high-torque and lightweight actuators suitable for lightweight robotic applications. The detailed design of the actuator module is described, and its performance evaluation is also presented. Further, the mathematical modelling of the actuator is discussed. Various performance analysis tests were carried out for the elucidation of the designed actuator, which included primarily position, velocity, and torque analyses. The position accuracy analysis included position repeatability at the maximum payload for calculating the acceptable tolerance. The velocity elucidation included a velocity test for the variable load. The torque analysis of the actuator was completed at different supply currents. These tests and the results indicate that the proposed actuator has high precision in reaching the desired position and provides a stabilized performance with variable loads up to the limit for which it was designed. Based on the torque output and the weight, the proposed actuator could be a good fit for lightweight robotic applications.

Development of Hydroacoustic Localization Algorithms for AUV Based on the Error-Corrected WMChan-Taylor Algorithm

Autonomous underwater vehicles (AUVs) are susceptible to non-line-of-sight (NLOS) errors and noise bias at receiving stations during the application of hydroacoustic localization systems, leading to a degradation in positioning accuracy. To address this problem, this paper optimizes the Chan-Taylor algorithm. Initially, we propose the Weighted Modified Chan-Taylor (WMChan-Talor) algorithm, which introduces dynamic weights into the Chan algorithm to correct noise variance at measurement stations, thereby improving the accuracy of AUV positioning. Computer simulations validate the effectiveness of the WMChan-Taylor algorithm in enhancing positioning accuracy. To further address the accuracy degradation caused by noise deviations across different receiving stations, we introduce an error-corrected WMChan-Taylor algorithm. This algorithm utilizes a standard residual function to eliminate significant delays caused by large errors at receiving stations and applies standard residual weighting to improve the combined positioning solution. The performance of the error-corrected WMChan-Taylor algorithm is demonstrated through both computer and semi-physical simulation experiments, confirming its capability to isolate noisier stations and thus enhance overall positioning accuracy.