Position Accuracy Research Articles

Benchmarking of monocular camera UAV-based localization and mapping methods in vineyards

UAVs equipped with various sensors offer a promising approach for enhancing orchard management efficiency. Up-close sensing enables precise crop localization and mapping, providing valuable a priori information for informed decision-making. Current research on localization and mapping methods can be broadly classified into SfM, traditional feature-based SLAM, and deep learning-integrated SLAM. While previous studies have evaluated these methods on public datasets, real-world agricultural environments, particularly vineyards, present unique challenges due to their complexity, dynamism, and unstructured nature.To bridge this gap, we conducted a comprehensive study in vineyards, collecting data under diverse conditions (flight modes, illumination conditions, and shooting angles) using a UAV equipped with high-resolution camera. To assess the performance of different methods, we proposed five evaluation metrics: efficiency, point cloud completeness, localization accuracy, parameter sensitivity, and plant-level spatial accuracy. We compared two SLAM approaches against SfM as a benchmark.Our findings reveal that deep learning-based SLAM outperforms SfM and feature-based SLAM in terms of position accuracy and point cloud resolution. Deep learning-based SLAM reduced average position error by 87% and increased point cloud resolution by 571%. However, feature-based SLAM demonstrated superior efficiency, making it a more suitable choice for real-time applications. These results offer valuable insights for selecting appropriate methods, considering illumination conditions, and optimizing parameters to balance accuracy and computational efficiency in orchard management activities.

Underwater-to-air distorted image correction based on the reconstructed water surface

Abstract The lights with spatial target information received by the underwater camera are refracted at the intersection with the water surface, resulting in geometric distortion of the image. Existing methods for correcting the water-to-air distorted images typically rely on a large amount of data, such as image sequences, making the restoration using a single frame challenging. To address the issue, we propose a spatial pixel correction algorithm based on the reconstructed water surface. Firstly, we introduce a gradient water surface reconstruction algorithm based on the discrete normal vector, ensuring high accuracy in the spatial position and amplitude of the reconstructed water surface. Thus, intersections of the lights with the reconstructed water surface can be solved based on the constructed water surface. Subsequently, we propose a camera’s reverse tracking algorithm, which skillfully links the images with the spatial pixel coordinates. Finally, based on the characteristics of pixel arrangement, we propose a spatial grid algorithm to separate the spatial coordinates obtained by the reverse tracking algorithm. This part can better handle the highly concentrated and over-dispersed pixels in the spatial coordinate system. The proposed correction algorithm has better correction performance. The similarity between the restored and real images is more than 80%, and the mean square error is less than 0.01.

Geometric feature extraction and its profile accuracy evaluation for pockets on curved aircraft skin from scanned point clouds

Abstract Dimension inspection is crucial for aircraft skin manufacturing and assembly. To meet weight loss and other functional requirements, large numbers of shallow pockets are distributed on the surface of thin-walled aircraft skin. It is difficult to evaluate the profile accuracy of the pockets, as the aircraft skin is composed of curved surfaces and its actual geometry is inconsistent with the nominal model. This study proposes a method to evaluate the profile and position accuracy for the pockets on aircraft skin utilizing point clouds obtained by 3D scanners. Firstly, the feature contour lines are identified and extracted from the scanned aircraft skin surface point clouds by adopting the local curvature-aware method and minimum bounding rectangle algorithm. Then, an algorithm is proposed to mitigate the contour identification deviations of the segmented area by employing the shortest geodesic distance and transition zone section projection, so as to enhance the measurement accuracy. Finally, the viability of the proposed method for evaluating pockets on aircraft skin is verified by the simulation, and the reliability and robustness of the proposed method are verified by the simulation and experiment. Experimental results show that the measurement accuracy of the pocket can be improved from 0.5 mm to 0.0505 mm, compared with traditional specialized conformation fixture methods.

Better accuracy of robotic-assisted total knee arthroplasty compared to conventional technique in patients with failed high tibial osteotomy.

This study aimed to compare the clinical outcomes, mechanical axis, component positioning, leg length discrepancy (LLD), and polyethylene liner thickness between robotic-assisted total knee arthroplasty (TKA) and conventional TKA in patients with failed high tibial osteotomy (HTO). A total of 30 patients (30 knees) with failed HTO who underwent TKA using a robot-assisted system between June 2020 and December 2023 were included in this study (robotic group). Additionally, 60 patients (60 knees) with failed HTO who underwent conventional TKA were included as controls (conventional group). Propensity score matching was performed using a 2:1 ratio between the matched participants. The mean follow-up period was 2.1 years in the robotic group and 2.2 years in the conventional group. Clinical evaluations were performed using the Knee Society Score (KSS) rating system. Mechanical axis, component coronal and sagittal positioning, and LLD were evaluated using postoperative radiographs. The thickness of the polyethylene liner was also determined. The mean error values and outliers were calculated and compared between the two groups to determine the accuracy of the mechanical axis, postoperative component positioning, and LLD. The postoperative KSSs in the robotic and conventional groups were not statistically different. The robotic group achieved better accuracy than the conventional group in terms of postoperative mean mechanical axis (1.7° vs. 2.4°, p < 0.05), femur coronal inclination (90.0° vs. 91.6°, p < 0.05), tibial coronal inclination (90.3° vs. 91.3°, p < 0.05), tibial sagittal inclination (90.5° vs. 91.4°, p < 0.05), and LLD (2.2 vs. 7.0 mm, p < 0.05). A significant difference in polyethylene liner thickness was observed between the two groups (p < 0.05). Robotic-assisted TKA showed improved mechanical axis, higher accuracy of component positioning and polyethylene liner thickness, and reduced LLD compared with those of conventional TKA in patients with failed HTO. Further studies with a larger sample size and long-term follow-up are warranted to ascertain whether the accuracy of robotic-assisted TKA can translate into better clinical outcomes and patient satisfaction.

A Fuzzy Logic Approach to Enhance GPS Accuracy for Blood Cooler Box Tracking

An innovative technological development, the position tracking system uses latitude and longitude coordinates to determine the GPS's location. The covid-19 pandemic has caused the demand for cool box delivery for convalescent blood plasma to increase significantly. The obstacle in the field is the hospital's position and patient rooms far from the reach convalescent donors, so they require a cool box for delivery. The problem is tracking the cool box accurately and precisely so that it can be monitored by the hospital properly. For this reason, it is necessary to increase the accuracy of the GPS position in the cool box by using fuzzy logic algorithms The Ublox NEO-6M is one GPS module that can be utilized for navigation. This module uses UART connection using the NMEA 0183 protocol and has an accuracy level of around 2.5 meters to 10 meters. In this research, validation of the accuracy of the GPS coordinate position was carried out on a Blood Cool Box device which was designed using the fuzzy logic method. The Sugeno method fuzzy logic algorithm is used to validate the accuracy of GPS coordinate positions based on latitude and longitude obtained from the GPS sensor module. The test results show a Mean Absolute Percent Error (MAPE) value of 21.66% which can be concluded that the Sugeno fuzzy logic method algorithm has forecasting model capabilities that are suitable for use as a validation method for testing GPS position coordinates.

Research on an Algorithm for High-Speed Train Positioning and Speed Measurement Based on Orthogonal Time Frequency Space Modulation and Integrated Sensing and Communication

The Doppler effect caused by the rapid movement of high-speed rail services has a great impact on the accuracy of train positioning and speed measurement. Existing train positioning algorithms require a large number of trackside equipment and sensors, resulting in high construction and maintenance costs. Aiming to solve the above two problems, this article proposes a train positioning algorithm based on orthogonal time–frequency space (OTFS) modulation and integrated sensing and communication (ISAC). Firstly, based on the OTFS, the positioning and speed measurement architecture of communication awareness integration is constructed. Secondly, a two-stage estimation (TSE) algorithm is proposed to estimate the delay Doppler parameters of HST. In the first stage, a low-complexity coarse grid search is used, and in the second stage, a refined off-grid search is used to obtain the delay Doppler parameters. Then, the time difference of arrival/frequency difference of arrival (TDOA/FDOA) algorithm based on multiple base stations is used to locate the target, the weighted least square method is used to calculate the location, and the Cramér–Rao lower bound (CRLB) for positioning and speed measurement is derived. The simulation results demonstrate that, compared to GNSS/INS and OFDM radars, the algorithm exhibits enhanced positioning and speed measurement accuracy.

A survey of offline precision calibration of industrial robots

Abstract. As the need for precision and efficiency grows in industrial manufacturing, the accuracy of industrial robots assumes paramount importance. Nevertheless, factors, such as mechanical structures and assembly processes often introduce errors, compromising robots' positioning and repeatability accuracy. Consequently, precise calibration and compensation of these errors are paramount for optimizing robots performance. A thorough analysis of error sources and the establishment of error models in industrial robots are conducted in the present study, with special attention paid to offline calibration methods. This paper reviews the robot error model building methods and offline accuracy calibration techniques, summarizes the technical difficulties of the calibration task, and proposes the future development direction for the difficulties such as the complexity of error modeling, the difficulty of non-motor calibration calculation, and the traditional stiffness compensation that does not meet the needs of the robot's operation process. The importance of this research lies in its potential to improve the accuracy and reliability of industrial robots, thus contributing to the development of industrial automation.

Development of an Automatic Harvester for Wine Grapes by Using Three-Axis Linear Motion Mechanism Robot

In Japan, the aging and decreasing number of agricultural workers is a significant problem. For wine grape harvesting, especially for large farming areas, there is physical strain to farmers. In order to solve this problem, this study focuses on developing an automated harvesting robot for wine grapes. The harvesting robot needs high dust, water, and mud resistance because grapevines are grown in hard conditions. Therefore, a three-axis linear robot was developed using a rack and pinion mechanism in this study, which can be used in outdoor conditions with low cost. Three brushless DC motors were utilized to drive the three-axis linear robot. The motors were controlled using a control area network (CAN) bus to simplify the hardware system. The accuracy of the robot positioning was evaluated at the automated harvesting condition. The experiment results show that the accuracy is approximately 5 mm, 9 mm, and 9 mm in the x-axis (horizontal), y-axis (vertical), and z-axis (depth), respectively. In order to improve the accuracy, we constructed an error model of the robot and conducted a calibration of the robot. The accuracy was improved to around 2 mm of all three axes after calibration. The experimental results show that the accuracy of the robot is high enough for automated harvesting of the wine grapes.

A vessel bifurcation liver CT landmark pair dataset for evaluating deformable image registration algorithms.

Evaluating deformable image registration (DIR) algorithms is vital for enhancing algorithm performance and gaining clinical acceptance. However, there is a notable lack of dependable DIR benchmark datasets for assessing DIR performance except for lung images. To address this gap, we aim to introduce our comprehensive liver computed tomography (CT) DIR landmark dataset library. This dataset is designed for efficient and quantitative evaluation of various DIR methods for liver CTs, paving the way for more accurate and reliable image registration techniques. Forty CT liver image pairs were acquired from several publicly available image archives and authors' institutions under institutional review board (IRB) approval. The images were processed with a semi-automatic procedure to generate landmark pairs: (1) for each case, liver vessels were automatically segmented on one image; (2) landmarks were automatically detected at vessel bifurcations; (3) corresponding landmarks in the second image were placed using two deformable image registration methods to avoid algorithm-specific biases; (4) a comprehensive validation process based on quantitative evaluation and manual assessment was applied to reject outliers and ensure the landmarks' positional accuracy. This workflow resulted in an average of ∼56 landmark pairs per image pair, comprising a total of 2220 landmarks for 40 cases. The general landmarking accuracy of this procedure was evaluated using digital phantoms and manual landmark placement. The landmark pair target registration errors (TRE) on digital phantoms were 0.37±0.26 and 0.55±0.34mm respectively for the two selected DIR algorithms used in our workflow, with 97% of landmark pairs having TREs below 1.5mm. The distances from the calculated landmarks to the averaged manual placement were 1.27±0.79mm. All data, including image files and landmark information, are publicly available at Zenodo (https://zenodo.org/records/13738577). Instructions for using our data can be found on our GitHub page at https://github.com/deshanyang/Liver-DIR-QA. The landmark dataset generated in this work is the first collection of large-scale liver CT DIR landmarks prepared on real patient images. This dataset can provide researchers with a dense set of ground truth benchmarks for the quantitative evaluation of DIR algorithms within the liver.

Is it possible to generate accurate 3D point clouds with UAS-LIDAR and UAS-RGB photogrammetry without GCPs? A case study on a beach and rocky cliff

ContextRecently, Unoccupied Aerial Systems (UAS) with photographic or Light Detection and Ranging (LIDAR) sensors have incorporated on-board survey-grade Global Navigation Satellite Systems that allow the direct georeferencing of the resulting datasets without Ground Control Points either in Real-Time (RTK) or Post-Processing Kinematic (PPK) modes. These approaches can be useful in hard-to-reach or hazardous areas. However, the resulting 3D models have not been widely tested, as previous studies tend to evaluate only a few points and conclude that systematic errors can be found.ObjectivesWe test the absolute positional accuracy of point clouds produced using UAS with direct-georeferencing systems.MethodsWe test the accuracy and characteristics of point clouds produced using a UAS-LIDAR (with PPK) and a UAS-RGB (Structure-from-Motion or SfM photogrammetry with RTK and PPK) in a challenging environment: a coastline with a composite beach and cliff. The resulting models of each processing were tested using as a benchmark a point cloud surveyed simultaneously by a Terrestrial Laser Scanner.ResultsThe UAS-LIDAR produced the most accurate point cloud, with homogeneous cover and no noise. The systematic bias previously observed in the UAS-RGB RTK approaches are minimized using oblique images. The accuracy observed across the different surveyed landforms varied significantly.ConclusionsThe UAS-LIDAR and UAS-RGB with PPK produced unbiased point clouds, being the latter the most cost-effective method. For the other direct georeferencing systems/approaches, the acquisition of GCP or the co-registration of the resulting point cloud is still necessary.

FPGA-Based High-Frequency Voltage Injection Sensorless Control with Novel Rotor Position Estimation Extraction for Permanent Magnet Synchronous Motor

This study developed a realization of sensorless control for a permanent magnet synchronous motor (PMSM) using a field-programmable gate array (FPGA). Both position and speed were estimated using a high-frequency (HF) injection scheme. Accurate estimation is essential to ensure the proper functioning of sensorless motor control. To improve the estimation accuracy of the rotor position and reduce the motor speed ripple found in conventional methods, a new extraction strategy for estimating the rotor position and motor speed is proposed. First, signal modulation compensation was applied to expand the information of the error function in order to provide more accurate data to the tracking loop system for rotor position extraction. Second, to minimize the motor speed ripple caused by the HF injection, motor speed estimation was performed after obtaining the rotor position information using a differential equation with a low-pass filter (LPF) to avoid the direct effect of the injected signal. Verified experimentally, the results showed that the rotor position error did not exceed 10 el.deg, so these methods effectively reduce the rotor position estimation error by about 30%, along with the motor speed ripple. Therefore, better performance in sensorless PMSM control can be achieved in motor control applications.

Research on Digital Construction Technology for Special-Shaped Shell Pipe Structures

The aesthetic appeal of special-shaped shell pipe structures makes them highly favored by architects and holds promising prospects for various applications. In the detailed design stage, NURBS curves should be divided into multiple continuous arcs due to the limitations of current steel structure fabrication equipment, which can only accommodate pipes with equal-curvature bends. However, the traditional manual fitting methods suffer from several issues including low efficiency, undercutting at the interface, poor smoothness of curves, and lack of control over tolerances. Furthermore, the weaker out-of-plane stiffness and utilization of bending arc pipe sections pose significant challenges in terms of spatial positioning and installation accuracy that need to be addressed. The study focuses on addressing these challenges by investigating digital construction technology for special-shaped shell pipe structures and developing a parametric algorithm that enables automatic fitting of spatial NURBS curves into multiple arcs, thereby achieving seamless curve fitting. A post-processing program was developed to enable the parametric generation of fabrication and installation information for structural members, which can be seamlessly integrated into the BIM database. Finally, structural position control technology is proposed to improve assembly efficiency and ensure consistency between the completed construction state and the design shape. The above digital construction technology has been applied in projects such as the Haihua Island International Conference Center. It can provide complete technical solutions for modeling of special-shaped shell pipe structures, including establishment of a member information database, fabrication at the workshop and installation on site, construction organization management, as well as installation accuracy control.

Evaluating Alternative Registration Planes in Imageless, Computer-Assisted Navigation Systems for Direct Anterior Total Hip Arthroplasty.

(1) Background: Imageless computer navigation systems have the potential to improve the accuracy of acetabular cup position in total hip arthroplasty (THA). Popular imageless navigation methods include locating the patient in a three-dimensional space (registration method) while using a baseline to angle the acetabular cup (reference plane). This study aims to compare the accuracy of different methods for determining postoperative acetabular cup positioning in THA via the direct anterior approach. (2) Methods: Fifty-one participants were recruited. Optical and inertial sensor imageless navigation systems were used simultaneously with three combinations of registration methods and reference planes: the anterior pelvic plane (APP), the anterior superior iliac spine (ASIS) and the table tilt (TT) method. Postoperative acetabular cup position, inclination, and anteversion were assessed using CT scans. (3) Results: For inclination, the mean absolute error (MAE) was lower using the TT method (2.4° ± 1.7°) compared to the ASIS (2.8° ± 1.7°, p = 0.17) and APP method (3.7° ± 2.1°, p < 0.001). For anteversion, the MAE was significantly lower for the TT method (2.4° ± 1.8°) in contrast to the ASIS (3.9° ± 3.2°, p = 0.005) and APP method (9.1° ± 6.2°, p < 0.001). (4) Conclusion: A functional reference plane is superior to an anatomic reference plane to accurately measure intraoperative acetabular cup inclination and anteversion in THA using inertial imageless navigation systems.

Research on intelligent cutting control technology of transverse moving machine for large cross-section roadheader

With the continuous development of the coal mine industry in our country, the cross-section of the roadway is getting larger and larger. The widely used EBZ series roadheader cannot complete the roadway cutting operation with a large cross-section at one time, so it usually needs to move the equipment left and right to complete all its cutting tasks. This paper studies the intelligent control technology of large cross-section roadway cutting, which mainly solves the positioning and attitude determination technology of roadheader in the large cross-section, and offers an accurate shape-cutting control technology and a path planning technology using a lateral moving machine. This paper proposes to combine the signals of laser sensor and fiber-optic inertial navigation system to form a combined positioning and attitude determination system, so as to make use of the advantages and avoid the disadvantages of their respective systems, and improve the accuracy of roadheader positioning and attitude determination. A swing kinematics model of the cutting arm is established, based on which the precise motion control of the first roadway section cutting and the second roadway section cutting can be realized. A key parameter calculation model of roadheader lateral moving machine is put forward to calculate the operation parameters for roadheader lateral moving machine path planning. Finally, the experimental research on intelligent cutting control technology of large cross-section roadheader is carried out in 12307 intelligent heading face of Wangjialing Coal Mine. The results show that the positioning error of the combined positioning and attitude determination system proposed in this paper is between 50-90 mm in the X-axis direction and between 25-45 mm in the Y-axis direction. The average attitude measurement error of heading angle is between 0.5 and 1.5°. This makes the technology appropriate for engineering use. The studied cutting control system achieves a cutting error of 25-50 mm for the first section, and a cutting error of 40-90 mm for the complete section. The complete section cutting error meets the ±100 mm cutting error requirement required in engineering. The intelligent cutting control technology of the lateral moving machine of the large-section tunnel boring machine studied in this paper can realize accurate cutting of the large-section tunnel of 5.6 meters. The research results of this paper provide a reference for the intelligent construction of heading faces.

Evaluating the Effectiveness of ICESat-2 ATL03 Data for Georeferencing Satellite Images

Abstract. ICESat-2 laser points, as a kind of public geographic data, have high ranging accuracy and have great potential in improving the geometric positioning accuracies of satellite images. This paper aims to evaluate the effectiveness of ICESat-2 ATL03 data for georeferencing satellite image. Our study combines ATL03 laser points with an image basemap processed using open-source data positioning to construct digital control photo (DCP), facilitating the rapid transformation of laser points into laser control points for image georeferencing. Through a comprehensive analysis involving comparison with traditional georeferencing methods using ground control points (GCPs), our findings indicate a significant enhancement in accuracy and efficiency in georeferencing, showcasing the potential of ICESat-2 data in improving the quality and reliability of remote sensing images. Experiments using the extracted laser points and GeoEye-1 images to construct DCP for georeferencing IKONOS images in Hobart, Australia demonstrate that the proposed method improved the horizontal accuracy from 2.33 meters to 0.97 meters and the vertical accuracy from 3.44 meters to 0.59 meters, compared to the georeferencing method without GCPs.

The Aerodynamic Assessment and Shot Point Position Accuracy of UAV Seismic Source

An unmanned aerial vehicle (UAV) seismic source, suitable for complex terrains due to its eco-friendly, cost-effective, and efficient nature, operates by hovering and releasing impact objects to generate seismic waves. The aerodynamic behavior of these impact objects is vital for shotpoint position accuracy and drag magnitude. Excessive drag will result in the loss of seismic source energy, whereas the deviations of the shotpoint position will disrupt the geometric structure of the observation system, significantly impacting the quality of seismic data acquisition. Through theoretical calculations, the influencing factors of air resistance and cross-wind deflection are analyzed. Air resistance increases with the increase in mass, cross-sectional area, speed, and drag coefficient. Increasing the mass and throwing height can increase the upper limit of the output, but after the air resistance and gravity are balanced, the output kinetic energy of the drone’s seismic source will no longer increase when the throwing height is increased. Using computational fluid dynamics simulations and field tests, this study examines the aerodynamics of four prevalent impact object designs. The results show that shuttle and spherical structures have the least drag during descent and are less influenced by crosswinds, thus being optimal for this application. Field measurement data are also presented for reference.

Improved analytical solution with optimization constraints using TDOA and FDOA measurements for USV/AUV collaborative localization

The paper introduces an enhanced constrained two-step weighted least squares (WLS) analytic localization algorithm designed to determine the AUV’s position and velocity within the collaborative localization system, utilizing time difference of arrival and frequency difference of arrival measurements. This algorithm integrates optimization constraints between intermediate variables and target motion parameters, producing precise analytical solution based on WLS minimization criterion. The proposed process not only reduces computational burden by providing an analytical solution but also enhances estimation accuracy through the incorporation of optimization constraints. Analytical assessments and statistical simulations highlight the superior estimation accuracy of the proposed algorithm, and demonstrate that the position and velocity estimation accuracy align with the Cramér-Rao lower bound under appropriate measurement noise conditions.

Comprehensive analysis of different GNSS receivers performance based on PPP-AR and positioning accuracy during 22 geomagnetic storms in 2023

Geomagnetic storms induced ionospheric disturbances can degrade the positioning accuracy and Ambiguity Resolution (PPP-AR) of GPS Precise Point Positioning (PPP), and this negative effect varies among different GNSS receivers. Under current conditions with frequent geomagnetic storms occurrences during the peak of solar cycle 25, selecting GNSS receivers with strong resistance to ionospheric disturbances is meaningful for precise GNSS positioning and ionospheric research. Therefore, it is necessary to conduct a comprehensive comparison and evaluation of the performance for different GNSS receivers under geomagnetic storms. Based on 22 geomagnetic storms in 2023, we investigated the three-dimensional root mean square error (3D RMS) and ambiguity resolved percentage (ARP), which is defined as the ratio of the number of carrier-phase observations with fixed ambiguities to the total number of phase measurements of GPS kinematic PPP, across ten groups of collocated stations around the world. For the groups using different receiver brands, the performance ranking for positioning accuracy and PPP-AR during geomagnetic storms are as follows: TPS > JVAVD > SEPTENTRIO > TRIMBLE. The station with a larger average ARP generally has a smaller average 3D RMS error than the another GNSS station. The average 3D RMS error between collocated stations using different receiver brands is typically greater than 0.010 m and even larger. The largest 3D RMS error difference is observed between collocated stations with SEPTENTRIO and JAVAD receivers, with 3D RMS errors of 0.089 m and 0.019 m, respectively. These identical-brand receivers with different types are as follows: TPS NET-G5 > TPS NET-G3A, TRIMBLE ALLOY > TRIMBLE NETRS, SEPT POLARX5TR > SEPT POLARX5S, and LEICA GR10 > LEICA GR30, respectively. The average differences in 3D RMS and ARP can reach up to 0.021 m and 6.0%, respectively. We found that the choice of antennas does not significantly affect PPP positioning performance during storms, with differences in average 3D RMS below 0.005 m and ARP differences below 0.2%.. Higher latitudes have more satellites affected by ionospheric disturbances, while this effect is typically only observed during strong storms (Dstmin≤-100nT) in mid-latitudes. Adequate available GPS satellites are crucial for achieving high PPP-AR and positioning accuracy during storms. The variations of disturbed satellites and the average Rate of Total Electron Content Index (ROTI) for collocated stations are generally consistent. However, there are differences in the average of ROTI values for different receivers under geomagnetic storms. The differences in PPP-AR performance and ROTI values among different receivers under geomagnetic storms can be attributed to the variations in satellite tracking algorithms, tracking loop designs, firmware, and multipath mitigation methods used by different receivers. These findings can provide a reference value and future research direction for selection, application and enhancement for different receivers to achieve GNSS high precision positioning under complex space weather conditions.

Population records reveal expanded habitat preference for the endemic, predaceous Jamaican fungus gnat Neoditomyia farri (Diptera, Keroplatidae).

The predaceous fly Neoditomyia farri Coher is currently regarded as having an unknown population distribution in Jamaica. The larvae are known for their production of sticky "fishing lines" that are covered in adhesive droplets for prey capture and hang down from cave walls and ceilings. Published research beyond early observational records of the species is limited to one site-Dromilly Cave in the parish of Trelawny. The Jamaican Caves Organisation has observed larvae of the species at 26 caves visited over the last 2 decades and compiled a database with details of approximate numbers, internal and external environmental conditions, and accurate positions. Based on these records, we present evidence that the species is not cave obligate and describe factors that may suggest potential additional sites where the species can be found. Taken together, our observations paint a picture of a species which is ubiquitous and not under discernible threat, making it an excellent candidate for further research on its unique life history and behavior, which remain poorly understood.

Robotic pedicle screw placement with 3D MRI registration: moving towards radiation free robotic spine surgery

BACKGROUND CONTEXTPreoperative imaging for lumbar spine surgery often includes magnetic resonance imaging (MRI) for soft tissues and computer tomography (CT) for bony detail. While CT scans expose patients to ionizing radiation, whereas MRI scans do not. Emerging MRI techniques allow CT-like 3-dimensional (3D) visualization of bony structures, potentially removing the need for ionizing radiation from CT scans. PURPOSEThis study aims to explore the accuracy of robot-assisted lumbar pedicle screw placement based on preoperative CT-like 3D MRI as the data source for robotic registration. STUDY DESIGNHuman cadaveric study. METHODSCT-like 3D MRI scans of the lumbar spine were acquired in ten human cadavers. A robotic navigation platform was used to plan and navigate pedicle screw placement based on the CT-like 3D MRI. Postoperative CT scans assessed the accuracy of screw positioning compared to preoperative planning based on the Gertzbein-Robbins scale (GRS) and by direct measurement (mm). RESULTSA total of 100 lumbar pedicle screws were robotically placed in ten cadavers (L1 through L5 bilaterally) based on CT-like 3D MRI. On postoperative CT evaluation, 99.0% of the positioned screws achieved an acceptable grade on the GRS (Grade A: n=89 or Grade B: n=10), with 89.0% classified as Grade A and 10.0% as Grade B. Meaning that 89.0% of screws were fully contained within the pedicle (GRS A), and 10% had a minor cortical breach <2 mm (GRS B). The median deviation from the planned trajectory was 0.2 mm (axial IQR: 0.1 to 0.5 mm; sagittal: IQR: 0.1 to 0.4 mm), in both axial and sagittal planes. CONCLUSIONThis study showed that image registration of CT-like 3D MRI for robotic-assisted spine surgery is technically feasible and that accurate pedicle screw placement can be achieved without preoperative CT. Each CT-like 3D MRI was successfully registered for robotic navigation. CLINICAL SIGNIFICANCEThe results suggest that CT-like 3D MRI has the potential to be a radiation-free alternative for preoperative planning and navigation in lumbar spine instrumentation procedures.