Calibration Grid Research Articles

Calibration of Marine Pressure Sensors with a Combination of Temperature and Pressure: A Case Study of SBE 37-SM

Accurate pressure measurement is crucial for understanding ocean dynamics in marine research. However, pressure sensors based on strain measurement principles are significantly affected by temperature variations, impacting the accuracy of depth measurements. This study investigates the SBE37-SM sensor and presents an improved calibration method based on a constant-pressure, variable-temperature scheme that effectively addresses temperature-induced deviations in pressure measurement. Experiments were conducted across a pressure range of 2000 dbar to 6000 dbar and a temperature range of 2 °C to 35 °C, establishing a comprehensive pressure–temperature calibration grid. The results show that, at a pressure of 6000 dbar, temperature-induced variations in readings for brand new SBE37-SM sensors can reach up to 9 dbar, while, for used sensors, they exceed 12 dbar, following a U-shaped trend. After applying a polynomial regression model for calibration, these variations were reduced to within ±0.5 dbar, significantly reducing the measurement uncertainty of the sensors in complex marine environments. This method underscores the necessity of further optimizing the CTD system’s temperature compensation mechanism during calibration and highlights the importance of regular calibration to minimize measurement uncertainty.

An experimental array setup to study the integration of biosonar and maneuvering flight in bats

Understanding biosonar-based flight control in bats remains a challenge for fundamental bioacoustics and holds promise for engineered flight-control systems, especially for highly maneuverable drones. To accomplish this, detailed data on bat flight is required that captures the variability across flight situations, individuals, and species. Hence, a flight tunnel has been constructed that provides enough space for natural flight maneuvers. Reconstructing the detailed geometry of freely maneuvering bats requires capturing a flying bat from many different angles to ensure freedom from occlusions. Ideally, this would also be done without placing artificial markers on the bat. Hence, a flight tunnel has been instrumented with arrays of 50 high-speed video cameras and 28 ultrasonic microphones that were all integrated within a modular canvas setup. The optical properties of each camera have been determined from multiple images taken of a panel with a calibration grid seen from different orientations. The spatial relationship between the cameras was estimated based on a large set of images containing random points created with an LED. Reconstructing the 3D geometries of the bats from large numbers of high-speed video frames requires an automated method due to the problem's complexity. To this end, deep-learning methods are currently under development.

E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras.

Event cameras triggered a paradigm shift in the computer vision community delineated by their asynchronous nature, low latency, and high dynamic range. Calibration of event cameras is always essential to account for the sensor intrinsic parameters and for 3D perception. However, conventional image-based calibration techniques are not applicable due to the asynchronous, binary output of the sensor. The current standard for calibrating event cameras relies on either blinking patterns or event-based image reconstruction algorithms. These approaches are difficult to deploy in factory settings and are affected by noise and artifacts degrading the calibration performance. To bridge these limitations, we present E-Calib, a novel, fast, robust, and accurate calibration toolbox for event cameras utilizing the asymmetric circle grid, for its robustness to out-of-focus scenes. E-Calib introduces an efficient reweighted least squares (eRWLS) method for feature extraction of the calibration pattern circles with sub-pixel accuracy and robustness to noise. In addition, a modified hierarchical clustering algorithm is devised to detect the calibration grid apart from the background clutter. The proposed method is tested in a variety of rigorous experiments for different event camera models, on circle grids with different geometric properties, on varying calibration trajectories and speeds, and under challenging illumination conditions. The results show that our approach outperforms the state-of-the-art in detection success rate, reprojection error, and pose estimation accuracy.

Surface Grid Calibration of Line Structured Light Based on Ray-Tracing

Surface Grid Calibration of Line Structured Light Based on Ray-Tracing

A high-accuracy position and orientation measurement method for bolter-miner based on double-screen visual target

In this paper, a position and orientation measurement method for bolter-miner based on double-screen visual target is proposed, in which the target is composed of two industrial cameras and photosensitive surfaces. The point-cloud data of 2D–3D coordinates is established by using the method of grid calibration and index. Consequently, the posture parameters of bolter-miner and the horizontal/vertical offset distance of machine body key-points relative to the laneway tunneling axis (LTA) can be finally calculated with the prior information of the indicating laser. The experimental results show that the static repeatability measurement accuracy of laser-spot 3D coordinate is 0.07 mm, the repeatability measurement accuracy of yaw angle is better than 0.01°, the absolute measurement error of yaw angle is less than 0.05°, and the measurement error of horizontal/vertical offset distance are less than 5 mm. The experiment results proved that this method is practicable for application of the guidance and localization for bolter-miner.

Parameter Identification of Ice Drift toward Cross-River Bridges in Cold Regions

Ice load is one of the main loads that needs to be considered in the design and construction of wading bridges (bridges whose piers are submerged in water) in cold regions, and ice velocity, area, and thickness are the main parameters that affect ice load. A noncontact measurement technology based on the “ scale method” was proposed to realize small-range monitoring of river ice by laying calibration grids on the ice surface. The “bounding rectangle method” and Harris corner detection were used to accurately identify the area and velocity of a single ice drift. Also, the thickness of ice drift was estimated by statistical analysis. Considering the Huma River basin in the Daxing’an Mountains as the research object, field tests were performed to determine the flow ice parameters. The results indicate that noncontact measurement technology can accurately identify the velocity and area of ice drift and has wide application prospects, thereby providing a reference for the structural design and safety evaluation of wading bridges in cold areas.

SpatDistCalib: a GUI Python software for spatial-distortion correction of 2D detectors using splines.

CCD-based X-ray detector systems often suffer from spatial distortions. Reproducible distortions can be quantitatively measured with a calibration grid and described as a displacement matrix or as spline functions. The measured distortion can be used afterwards to undistort raw images or to refine the actual position of each pixel, e.g. for azimuthal integration. This article describes a method using a regular grid, not necessarily orthogonal, to measure the distortions. The graphical user interface (GUI) Python software that is used to implement this method is available under a GPLv3 license on ESRF GitLab, and produces a spline file that is usable with data-reduction software such as FIT2D or pyFAI.

Accurate Calibration of a Large Field of View Camera with Coplanar Constraint for Large-Scale Specular Three-Dimensional Profile Measurement

In the vision-based inspection of specular or shiny surfaces, we often compute the camera pose with respect to a reference plane by analyzing images of calibration grids, reflected in such a surface. To obtain high precision in camera calibration, the calibration target should be large enough to cover the whole field of view (FOV). For a camera with a large FOV, using a small target can only obtain a locally optimal solution. However, using a large target causes many difficulties in making, carrying, and employing the large target. To solve this problem, an improved calibration method based on coplanar constraint is proposed for a camera with a large FOV. Firstly, with an auxiliary plane mirror provided, the positions of the calibration grid and the tilt angles of the plane mirror are changed several times to capture several mirrored calibration images. Secondly, the initial parameters of the camera are calculated based on each group of mirrored calibration images. Finally, adding with the coplanar constraint between each group of calibration grid, the external parameters between the camera and the reference plane are optimized via the Levenberg-Marquardt algorithm (LM). The experimental results show that the proposed camera calibration method has good robustness and accuracy.

Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.

Spiking neural networks (SNNs) represent the state-of-the-art approach to the biologically realistic modeling of nervous system function. The systematic calibration for multiple free model parameters is necessary to achieve robust network function and demands high computing power and large memory resources. Special requirements arise from closed-loop model simulation in virtual environments and from real-time simulation in robotic application. Here, we compare two complementary approaches to efficient large-scale and real-time SNN simulation. The widely used NEural Simulation Tool (NEST) parallelizes simulation across multiple CPU cores. The GPU-enhanced Neural Network (GeNN) simulator uses the highly parallel GPU-based architecture to gain simulation speed. We quantify fixed and variable simulation costs on single machines with different hardware configurations. As a benchmark model, we use a spiking cortical attractor network with a topology of densely connected excitatory and inhibitory neuron clusters with homogeneous or distributed synaptic time constants and in comparison to the random balanced network. We show that simulation time scales linearly with the simulated biological model time and, for large networks, approximately linearly with the model size as dominated by the number of synaptic connections. Additional fixed costs with GeNN are almost independent of model size, while fixed costs with NEST increase linearly with model size. We demonstrate how GeNN can be used for simulating networks with up to 3.5 · 106 neurons (> 3 · 1012synapses) on a high-end GPU, and up to 250, 000 neurons (25 · 109 synapses) on a low-cost GPU. Real-time simulation was achieved for networks with 100, 000 neurons. Network calibration and parameter grid search can be efficiently achieved using batch processing. We discuss the advantages and disadvantages of both approaches for different use cases.

Reconciling spectroscopy with dynamics in global potential energy surfaces: The case of the astrophysically relevant SiC2.

SiC2 is a fascinating molecule due to its unusual bonding and astrophysical importance. In this work, we report the first global potential energy surface (PES) for ground-state SiC2 using the combined-hyperbolic-inverse-power-representation method and accurate ab initio energies. The calibration grid data are obtained via a general dual-level protocol developed afresh herein that entails both coupled-cluster and multi-reference configuration interaction energies jointly extrapolated to the complete basis set limit. Such an approach is specially devised to recover much of the spectroscopy from the PES, while still permitting a proper fragmentation of the system to allow for reaction dynamics studies. Besides describing accurately the valence strongly bound region that includes both the cyclic global minimum and isomerization barriers, the final analytic PES form is shown to properly reproduce dissociation energies, diatomic potentials, and long-range interactions at all asymptotic channels, in addition to naturally reflect the correct permutational symmetry of the potential. Bound vibrational state calculations have been carried out, unveiling an excellent match of the available experimental data on c-SiC2(A11). To further exploit the global nature of the PES, exploratory quasi-classical trajectory calculations for the endothermic C2 + Si → SiC + C reaction are also performed, yielding thermalized rate coefficients for temperatures up to 5000K. The results hint for the prominence of this reaction in the innermost layers of the circumstellar envelopes around carbon-rich stars, hence conceivably playing therein a key contribution to the gas-phase formation of SiC, and eventually, solid SiC dust.

Thin head atomic force microscope for integration with optical microscope.

We present a novel thin head atomic force microscope (AFM) that can be easily integrated with an upright optical microscope (OM). The optical beam detection unit in the AFM used an obliquely incident laser beam onto the cantilever, reducing the AFM head's effective thickness to 7.3mm. That allows an open space above the cantilever probe to accommodate the objective lens up to 0.6 numerical aperture (N.A.) without obstruction. A multi-function digital controller was developed to control the AFM and reserved interfaces to communicate with the OM. To assess the performance of the developed AFM, we first measured the noise level and bandwidths of the AFM system. Then, the imaging quality of the AFM was evaluated by both calibration grids and two-dimensional materials. Finally, the thin head AFM was integrated into a homemade white light interferometer as a demonstration of combined use with an advanced optical system. The experimental results demonstrated that our developed AFM is suitable for integration under upright OM and brings AFM high-resolution advantages to the existing OM system.

Optimised calibration of machine vision system for close range photogrammetry based on machine learning

Real-time inspection of large mechanical parts manufacturing using camera-based scanning systems are increasingly adopted in industry 4.0. It leads to take preventive actions during the manufacturing process and then to fabricate mechanical parts right-first-time with respect to specified tolerances.Therefore, the use of camera-based scanners requests a preliminary calibration process. It consists on estimating the intrinsic and extrinsic parameters required to relate the 3D world point to its projection on the image plane. Since selection of the calibration grid poses affect the calibration quality, one approach-based machine learning (ML-approach) is proposed including the polynomial approximation of the reprojection errors function of 6 degree of freedom (DoF) combined with particle swarm optimization (PSO). Synthetic and experimental evaluations have been performed while assessing the performance of the proposed ML-approach. The synthetic evaluation reveals a better convergence of the intrinsic and extrinsic parameters in comparison to recent published calibration methods by Wizard (CW-method) and Rojtberg (R-method). The experimental evaluation of the ML-approach shows an average error RE < 12 µm and a sub-micrometre repeatability, which confirm the benefit of using machine vision-based scanning systems for the inspection of large volume parts in real time.

A low-cost and real-time pose measurement method for straight pipe jacking machine based on dual-screen laser target in tunneling guidance

In this paper, a low-cost and real-time pose measurement method for straight pipe jacking machine in tunneling guidance is proposed, which utilizes an instruction laser and a dual-screen laser target. The precise localization of light-spot on target is realized by high-performance processing of the image and grid calibration of the target. The spatial matrix transformation method is used to calculate the parameters for the specified position of the pipe jacking machine. Subsequently, the horizontal/vertical deviation between the actual moving route of the straight pipe jacking machine and the design tunnel axis (DTA) can be calculated. The results of the experiment show that the precision and absolute error of the azimuth angle measurement are better than 0.03° (1∑) and 0.05°, respectively. The measurement error of horizontal/vertical deviation is less than 3 mm. Hence, this method is feasible and highly accurate, which can effectively solve the guidance problem of pipe jacking construction.

Learning to Calibrate - Estimating the Hand-eye Transformation Without Calibration Objects

Hand-eye calibration is a method to determine the transformation linking between the robot and camera coordinate systems. Conventional calibration algorithms use a calibration grid to determine camera poses, corresponding to the robot poses, both of which are used in the main calibration procedure. Although such methods yield good calibration accuracy and are suitable for offline applications, they are not applicable in a dynamic environment such as robotic-assisted minimally invasive surgery (RMIS) because changes in the setup can be disruptive and time-consuming to the workflow as it requires yet another calibration procedure. In this paper, we propose a neural network-based hand-eye calibration method that does not require camera poses from a calibration grid but only uses the motion from surgical instruments in a camera frame and their corresponding robot poses as input to recover the hand-eye matrix. The advantages of using neural network are that the method is not limited by a single rigid transformation alignment and can learn dynamic changes correlated with kinematics and tool motion/interactions. Its loss function is derived from the original hand-eye transformation, the re-projection error and also the pose error in comparison to the remote centre of motion. The proposed method is validated with data from da Vinci Si and the results indicate that the designed network architecture can extract the relevant information and estimate the hand-eye matrix. Unlike the conventional hand-eye approaches, it does not require camera pose estimations which significantly simplifies the hand-eye problem in RMIS context as updating the hand-eye relationship can be done with a trained network and sequence of images. This introduces a potential of creating a hand-eye calibration approach that is capable of accurately updating the hand-eye matrix according to the changes in RMIS setup.

Beam Scanning Controller for Proton-Beam Writing

A scanning control system of the ion beam of MeV energies has been developed for the nuclear scanning microprobe and proton-beam writing channel as a part of accelerator-analytical complex based on the Sokol electrostatic accelerator of the Institute of Applied Physics of the National Academy of Sciences of Ukraine. The system was put into operation to replace the obsolete one based on microcontrollers. The scanning control system is based on a National Instruments reconfigurable module with a Field Programmable Gate Array. The module operates in real time and is connected to a personal computer by a high-speed PCI-Express interface with data buffering. The system provides two main modes of operation: exposure of sample areas with a given profile and raster secondary electrons imaging of the sample or a calibration grid. Profile exposure is possible both in raster and functional scanning modes. Automatic calibration of the profile scale and scan raster is also implemented. Using of reconfigurable logic makes it possible to quickly adjust the system to the conditions of a particular experiment and the available equipment. The hardware capabilities of the scanning control system allows in the future to connect up to 4 spectrometric ADC for mapping the elemental composition of samples using Proton Induced X-ray Emission and Proton Backscattering. The first experiments on the irradiation of polymethylmethacrylate have been carried out; images of the obtained microstructures taken with a scanning electron microscope are shown. The aim of this work is to develop a control system for scanning a high-energy focused beam in proton beam writing technique to create small-sized structures for special purposes, as well as to demonstrate the efficiency of the developed system.

Calibration of CFD grid for simulating the impeller clearance flow and axial hydraulic force of centrifugal pump

The axial hydraulic force of centrifugal pump is an important parameter affecting pump performance. The force mainly includes the force inside impeller and in clearance. Due to the special structural characteristics of the clearance, the influence of grid discretization method on the calculation of axial force in the clearance is not fully understood. Therefore, based on the Reynolds-averaged method with shear stress transport turbulence model, an orthogonal experiment was designed to compare the correlation coefficient of velocity and pressure distribution between shear stress transport model and large eddy simulation models. A more suitable grid discretization strategy was found by artificial neural network for grid calibration. When the strategy is applied to the entire centrifugal pump, the prediction of axial force has high accuracy. The range analysis shows that the grid node number in the wall-wall direction has the greatest impact on velocity distribution. When the mesh parameters are in a certain range, it can compromise between the simulation accuracy and computational resource. The Reynolds-averaged model based simulation is proved accurate in capturing the complex velocity and pressure field inside clearance. The entire pump model is also used for the verification after the calibration of grid. The typical axial force law can be found under different flow rate conditions. This study provides a significant guidance in determining the grid scheme for accurate prediction of centrifugal pump’s axial force. It makes the computational fluid dynamics simulation feasible in the initial design of centrifugal pump which specifically considers the axial force problem.

3D Reconstruction with Single-Shot Structured Light RGB Line Pattern.

Single-shot 3D reconstruction technique is very important for measuring moving and deforming objects. After many decades of study, a great number of interesting single-shot techniques have been proposed, yet the problem remains open. In this paper, a new approach is proposed to reconstruct deforming and moving objects with the structured light RGB line pattern. The structured light RGB line pattern is coded using parallel red, green, and blue lines with equal intervals to facilitate line segmentation and line indexing. A slope difference distribution (SDD)-based image segmentation method is proposed to segment the lines robustly in the HSV color space. A method of exclusion is proposed to index the red lines, the green lines, and the blue lines respectively and robustly. The indexed lines in different colors are fused to obtain a phase map for 3D depth calculation. The quantitative accuracies of measuring a calibration grid and a ball achieved by the proposed approach are 0.46 and 0.24 mm, respectively, which are significantly lower than those achieved by the compared state-of-the-art single-shot techniques.

Optimized stereo digital image correlation setup for miniature round specimen: framework development and implementation

For the advancement of micro- and nano-technologies, multiaxial material testing at a small length scale is imperative. A novel multiaxial miniature testing system (MMTS) is under development for testing a tubular specimen of outer diameter (OD) as small as 1 mm. Because of the small specimen size of MMTS, stereo-Digital Image Correlation (DIC) is the preferred strain measurement technique. Although theoretically, stereo-DIC is length-scale independent, the implementation of stereo-DIC, particularly for miniature testing, faces experimental setup related challenges. For this reason, although stereo-DIC is strongly recommended over 2D DIC, researchers are sometimes compelled to use the latter. It is shown in the present study that the experimental setup related difficulties, particularly for miniature round specimen testing, can be overcome by a systematic development of a mathematical framework for stereo-DIC implementation. This framework addresses all setup decisions concerning stereo-DIC implementation, such as selections of stereo angle, speckle size, camera position, camera sensor size, lens focal length, dimensions of camera and lens bodies, calibration grid size, etc., as well as stereo-DIC analysis parameters, such as subset and step size. Besides serving the need of building a stereo-DIC setup for MMTS, since the developed mathematical framework treats all stereo-DIC setup decisions as variables, it can be used to develop an optimized stereo-DIC setup for any application. Examples of two general cases are reported. Since this general framework serves as a tool to solve the stereo-DIC experimental setup related challenges, the developed framework will contribute to the wider adoption of stereo-DIC over 2D DIC.

TEM image restoration from fast image streams.

Microscopy imaging experiments generate vast amounts of data, and there is a high demand for smart acquisition and analysis methods. This is especially true for transmission electron microscopy (TEM) where terabytes of data are produced if imaging a full sample at high resolution, and analysis can take several hours. One way to tackle this issue is to collect a continuous stream of low resolution images whilst moving the sample under the microscope, and thereafter use this data to find the parts of the sample deemed most valuable for high-resolution imaging. However, such image streams are degraded by both motion blur and noise. Building on deep learning based approaches developed for deblurring videos of natural scenes we explore the opportunities and limitations of deblurring and denoising images captured from a fast image stream collected by a TEM microscope. We start from existing neural network architectures and make adjustments of convolution blocks and loss functions to better fit TEM data. We present deblurring results on two real datasets of images of kidney tissue and a calibration grid. Both datasets consist of low quality images from a fast image stream captured by moving the sample under the microscope, and the corresponding high quality images of the same region, captured after stopping the movement at each position to let all motion settle. We also explore the generalizability and overfitting on real and synthetically generated data. The quality of the restored images, evaluated both quantitatively and visually, show that using deep learning for image restoration of TEM live image streams has great potential but also comes with some limitations.

Effect of Signal Propagation Model Calibration on Localization Performance Limits for Wireless Sensor Networks

In this paper, we focus on wireless sensor network-based localization for user devices (UDs). Prior to UD localization, signal propagation model (SPM) is required to calibrate using training samples from a number of location grids. However, SPM usually suffers from measurement noise and inevitable error in calibration grid (CG) locations. This will significantly degrade UD localization performance. Nevertheless, the impact of CG location error, CG layout and the number of CGs on SPM calibration performance has not been characterized. Furthermore, the effect of SPM calibration error on UD localization performance has not been developed. In this paper, we aim to provide a unified framework for performance analysis of SPM calibration and UD localization. Firstly, we establish a closed-form Cramér-Rao lower bound on SPM calibration error and UD localization error, respectively. Secondly, the impact of measurement noise, CG location error and the number of CGs on SPM calibration performance is revealed. Thirdly, the influence of SPM calibration error, CG location error and measurement noise on UD localization performance is studied. The effect of modeling mismatch is also studied. The obtained analysis framework builds a theoretical basis for the design of efficient system optimization strategies, including resource allocation and CG deployment optimization, for UD localization performance enhancement.