Self-calibration Method Research Articles

A Self-Calibration Method for Robot End-Effector Using Spherical Constraints

A self-calibration method utilizing spherical constraints is proposed for calibration of robot end-effectors. The method establishes a mathematical model to account for both the geometric errors of the robot and the deformation errors of the end-effector. A nonlinear least-squares parameter identification technique based on spherical constraints is employed to achieve autonomous calibration of the end-effector. Contrasted with methodologies relying on point plane or distance constraints, this novel technique delivers superior positioning accuracy, streamlined operational procedures and enhanced efficiency. Both simulation and experimental validation confirm that the self-calibration method using spherical constraints improves the positioning accuracy of the robot end-effector from 3 mm to 0.3 mm, showing the effectiveness of the method.

An optimization of the Fourier self-calibration algorithm for improved noise suppression capability

Abstract Self-calibration emerges as a promising approach in ultra-precision field, overcoming the limitations imposed by instrument capabilities in conventional calibration. In the self-calibration research, the algorithm based on the Fourier transform firstly established a rigorous theoretical framework. However, despite advancements in alternative self-calibration methods that have improved noise suppression, the traditional Fourier algorithm still faces challenges, particularly in mitigating random measurement noise in the separation results. In this study, we introduce methods on enhancing the noise suppression capabilities of the Fourier self-calibration algorithm. We investigate the hypothesis in the traditional algorithm and find that the computation of weighted averages had minimal effects. Then, we present our method by augmenting measurement positions. Considering both the increase in the number of measurement positions and the enhancement of noise suppression capabilities, augmenting translation positions in the opposite direction is proved to be the most effective strategy. Our algorithm can effectively suppress measurement noise when one-sided point number of the artifact is below 21. While the additional data processing slightly increases runtime, it does not alter the algorithm’s computational complexity, yet it significantly improves noise suppression. The effectiveness of our method is validated through a Monte Carlo comparison of uncertainties with the original algorithm. Experiments on Fizeau interferometer further prove robustness and practical feasibility of our proposed algorithm, with the measurement repeatability being reduced by 26.34% and 25.15% in the errors separated from the stage and the artifact.

Bayesian self-calibration and imaging in very long baseline interferometry

Context. Self-calibration methods with the CLEAN algorithm have been widely employed in very long baseline interferometry (VLBI) data processing in order to correct antenna-based amplitude and phase corruptions present in the data. However, human interaction during the conventional CLEAN self-calibration process can impose a strong effective prior, which in turn may produce artifacts within the final image and hinder the reproducibility of final results. Aims. In this work, we aim to demonstrate a combined self-calibration and imaging method for VLBI data in a Bayesian inference framework. The method corrects for amplitude and phase gains for each antenna and polarization mode by inferring the temporal correlation of the gain solutions. Methods. We use Stokes I data of M87 taken with the Very Long Baseline Array (VLBA) at43 GHz, pre-calibrated using the rPICARD CASA-based pipeline. For antenna-based gain calibration and imaging, we use the Bayesian imaging software resolve. To estimate gain and image uncertainties, we use a variational inference method. Results. We obtain a high-resolution M87 Stokes I image at 43 GHz in conjunction with antenna-based gain solutions using our Bayesian self-calibration and imaging method. The core with counter-jet structure is better resolved, and extended jet emission is better described compared to the CLEAN reconstruction. Furthermore, uncertainty estimation of the image and antenna-based gains allows us to quantify the reliability of the result. Conclusions. Our Bayesian self-calibration and imaging method is able to reconstruct robust and reproducible Stokes I images and gain solutions with uncertainty estimation by taking into account the uncertainty information in the data.

Reconstructing redshift distributions with photometric galaxy clustering

The accurate determination of the true redshift distributions in tomographic bins is critical for cosmological constraints from photometric surveys. The proposed redshift self-calibration method, which utilizes the photometric galaxy clustering alone, is highly convenient and avoids the challenges from incomplete or unrepresentative spectroscopic samples in external calibration. However, the imperfection of the theoretical approximation on broad bins as well as the flaw of the algorithm in previous work [1] risk the accuracy and application of the method. In this paper, we propose the improved self-calibration algorithm that incorporates novel update rules, which effectively accounts for heteroskedastic weights and noisy data with negative values. The improved algorithm greatly expands the application range of self-calibration method and accurately reconstructs the redshift distributions for various mock data. Using the luminous red galaxy (LRG) sample of the Dark Energy Spectroscopic Instrument (DESI) survey, we find that the reconstructed results are comparable to the state-of-the-art external calibration. This suggests the exciting prospect of using photometric galaxy clustering to reconstruct redshift distributions in the cosmological analysis of survey data.

Self-Calibration Method for RDC System of Gimbal Servo System in SGCMG Based on Signal Reconstruction

Self-Calibration Method for RDC System of Gimbal Servo System in SGCMG Based on Signal Reconstruction

A Study on Miniaturized In-Situ Self-Calibrated Thermometers Based on Ga and Ga-Zn Fixed Points.

In order to ensure the reliability and accuracy of long-term temperature measurement where the thermometers are discommodious or even impossible to access for conventional periodical calibration, a study on miniaturized in-situ self-calibrated (MISSC) thermometers based on Ga and Ga-Zn fixed points was conducted using temperature scale transfer technology. One MISSC thermometer consists of three parts: the first is the fixed-points hardware, including a container with two cells separately filled with Ga and Ga-Zn; the second is the temperature sensing hardware, made of a Type T thermocouple; the third is the mini-power heating hardware, made of a film resistance. The measurement and calibration (M&C) system comprises a temperature measurement and data processing subsystem and a mini-power heating control subsystem. Then, an in-situ self-calibration can be carried out by mini-power heating from a room temperature of about 20 °C, and then by comparison between the measured phase transition plateau results and the standard fixed-points, i.e., Ga fixed point (about 29.76 °C) and Ga-Zn fixed point (about 25.20 °C). A series of experiments were performed, and the results show that: (1) both the proposed hardware design and the self-calibration method are feasible, and (2) the Φ16 mm × 25 mm MISSC thermometer is found to be the most miniaturized one that can realize reliable self-calibration in this study.

Improving inter-session performance via relevant session-transfer for multi-session motor imagery classification

Motor imagery (MI)-based brain-computer interfaces (BCIs) using electroencephalography (EEG) have found practical applications in external device control. However, the non-stationary nature of EEG signals remains to obstruct BCI performance across multiple sessions, even for the same user. In this study, we aim to address the impact of non-stationarity, also known as inter-session variability, on multi-session MI classification performance by introducing a novel approach, the relevant session-transfer (RST) method. Leveraging the cosine similarity as a benchmark, the RST method transfers relevant EEG data from the previous session to the current one. The effectiveness of the proposed RST method was investigated through performance comparisons with the self-calibrating method, which uses only the data from the current session, and the whole-session transfer method, which utilizes data from all prior sessions. We validated the effectiveness of these methods using two datasets: a large MI public dataset (Shu Dataset) and our own dataset of gait-related MI, which includes both healthy participants and individuals with spinal cord injuries. Our experimental results revealed that the proposed RST method leads to a 2.29 % improvement (p < 0.001) in the Shu Dataset and up to a 6.37 % improvement in our dataset when compared to the self-calibrating method. Moreover, our method surpassed the performance of the recent highest-performing method that utilized the Shu Dataset, providing further support for the efficacy of the RST method in improving multi-session MI classification performance. Consequently, our findings confirm that the proposed RST method can improve classification performance across multiple sessions in practical MI-BCIs.

A Distortion Self-Calibration Method for Binocular High Dynamic Light Adjusting and Imaging System Based on Digital Micromirror Device

A Distortion Self-Calibration Method for Binocular High Dynamic Light Adjusting and Imaging System Based on Digital Micromirror Device

Self-calibration method for geometric parameters based on feature point projection trajectories in Cone-beam Computed Laminography

Cone-beam computed laminography (CBCL) is an X-ray three-dimensional imaging method that is suitable for large plate-like objects. Geometric parameter calibration is crucial during CL imaging to prevent geometric artifacts in the reconstructed image. This study introduced a self-calibration method employing feature-point projection trajectories for CL geometric parameters. The method constructed point projection trajectory models for the system’s geometric parameters and optimized the best fit of these models to the characteristic point projection trajectories to solve for the geometric parameters of the system. Furthermore, this study examined the impact of the feature point location in the object coordinate system on the parameter calibration accuracy and reduced the required feature point locations from 11°to 4°using the alternating optimization algorithm. The imaging results demonstrated the ability of the method to achieve high-accuracy calibration of CL system geometric parameters, reducing the calibration error of the rotational axis tilt angle α from 1.1% to 0.08% while exhibiting resilience to noise. The proposed method enabled the calibration of all CL system geometric parameters without the model calibration, laying the groundwork for rectifying image geometric artifacts and enhancing CL imaging quality. Moreover, this method presents a novel approach for CT system geometric parameter calibration.

Infrared Camera Array System and Self-Calibration Method for Enhanced Dim Target Perception

Infrared Camera Array System and Self-Calibration Method for Enhanced Dim Target Perception

Accuracy improvement of laser tracker registration based on enhanced reference system points weighting self-calibration and thermal compensation.

Improving the accuracy of large measurement systems consisting of multiple laser trackers and Enhanced Reference System (ERS) points is technically challenging. In practice, standard devices with precise distance limits are often used to improve the registration accuracy of laser trackers. However, these standard devices are expensive and need to be calibrated by the Coordinate Measuring Machine (CMM). In addition, the stability of ERS points can significantly affect registration errors. Therefore, this paper proposes a laser tracker registration method based on ERS point-weighted self-calibration and thermal deformation compensation. First, a self-calibration method for simple standard devices based on multilateration measurements is presented, which only utilizes large measurement systems without additional high-precision measurement instruments. Based on this, a weighted registration optimization algorithm for the registration process of a relocation laser tracker is proposed. Then, the position errors of ERS points caused by temperature changes are calculated and compensated based on the thermal deformation coefficient of large structural components. The compensated ERS points are used for the registration of the laser trackers. Finally, the effectiveness of the proposed method is demonstrated by a field measurement experiment on a large spherical shell. Compared with the most widely used benchmark method, the proposed method reduces the average registration error of all ERS points from 0.103 to 0.02mm.

Cross-wavelength calibrating method for real-time imaging of tissue optical properties using frequency-domain diffuse optical spectroscopy.

Frequency-domain diffuse optical spectroscopy (FD-DOS) is a powerful non-invasive technique for assessing tissue optical properties, with applications ranging from basic research to clinical diagnosis. In this study, we introduce and validate a novel approach termed the cross-wavelength calibrating (CWC) method within the framework of TrackDOSI, a real-time FD-DOS imaging system for tissue characterization. The CWC method aims to mitigate the effects of changing optical coupling and motion artifacts encountered during probe scanning, thus enhancing the accuracy and reliability of optical property measurements. Notably, the CWC method also allows for a simpler geometry with fewer sources than traditional self-calibrating (SC) methods, reducing instrumental complexity and cost while maintaining robustness in estimating optical properties. We first validate the CWC method on solid silicone phantoms, demonstrating strong agreement with the gold standard SC method with an error of -10% and 1% for absorption and reduced scattering coefficients, respectively. Furthermore, experiments on phantom and human tissue reveal the CWC approach's ability to suppress motion artifacts and optical coupling variations, thereby improving measurement repeatability, signal fidelity, and artifact correction in dynamic imaging scenarios. Our findings underscore the potential of the CWC method to enhance the clinical utility of DOSI techniques by enabling real-time artifact correction and improving the accuracy of tissue optical property measurements.

Design and Self-Calibration Method of a Rope-Driven Cleaning Robot for Complex Glass Curtain Walls

Rope-driven robots are increasingly used to safely and efficiently clean complex glass curtain walls. However, continuous global cleaning is difficult for most robots because of their poor performance in overcoming obstacles and adapting to curved surfaces, and an inconvenient winch calibration on complex surfaces further burdens such work. This paper presents a 3-DOF rope-driven robot and a winch self-calibration method for efficient cleaning. The robot, by integrating a 5-rope parallel configuration, a self-adaptive cleaning body, and a self-compensating driving winch, is designed to perform continuous, compliant, and accurate spatial motion on curved walls with obstacles. By deducing the kinematic model, the constraint relationship related to orderly arranged winch positions, maneuverable body positions, and accessible rope lengths is established during the robot tracking 3D trajectory. Combining the established relationship, a series of regulations are formulated for easily acquiring body positions and rope lengths, and then a self-calibration method is proposed by accurately calculating winch positions without using professional instruments. Experimental results show that the robot can perform global and precise movement on complex glass surfaces. Applying the proposed method, the maximum winch calibration error is 6.6 mm, and the maximum body tracking error is controlled within 9.6 mm.

A 18-bit 1-MS/s fully-differential SAR ADC with digital calibration achieving 96.1 dB SNDR

This paper presents a 18-bit 1 MS/s fully-differential Successive-Approximation-Register Analog-to-Digital Converter (SAR ADC) achieving an ENOB of 15.7-bit. To achieve higher performance, a differential DAC utilizing both split and monotonic switching timing is designed. For both dynamic and static performance improvement, five techniques are proposed. First, a foreground digital self-calibration method based on normalized-full-scale-referencing is described to eliminate the capacitor mismatch errors. L-segment capacitors are utilized to measure and calculate the bit weights of other capacitors. Second, a DNL enhancement technique is presented. The fractional capacitors are used to subtract an analog voltage from the DAC before the conversion is finished, which further improve the ADC performance. Third, an adaptive-tracking-extra-bit-trail together with comparator noise extraction and correction for further accuracy enhancement is proposed. Forth, a harmonic calibration technique, which can efficiently attenuate 2-order and 3-order harmonic is introduced. Fifth, a comparator with ultra-low noise and offset is designed to meet the 18-bit 1-MS/s ADC. The ADC is fabricated in a 0.18-μm 5-V CMOS process. It measures a 96.1 dB SNDR and a 110.7 dB SFDR. The DNL and INL are within ±0.32 LSB and ±0.5 LSB, respectively. The overall power consumption of ADC core, drawn from the 5 V power supply, is 45 mW.

Self-Calibrated Method for WMS Gas Sensor Immune to Optical and Electronic Drift

Self-Calibrated Method for WMS Gas Sensor Immune to Optical and Electronic Drift

Novel camera self-calibration method with clustering prior and nonlinear optimization from an image sequence

Novel camera self-calibration method with clustering prior and nonlinear optimization from an image sequence

An integrated cable-driven parallel robot for space station servicing: Self-calibration and pose estimation

This paper presents an integrated cable-driven parallel robot designed for space station servicing. All components, including winches, sensors, controllers, and others, are integrated within its envelope, greatly simplifying the installation process of the robot. However, practical application of this integrated robot necessitates a rapid self-calibration method that relies solely on internal sensors. A self-calibration method utilizing the inertial measurement unit embedded in the robot is introduced to identify external attachment point coordinates. The entire calibration process takes less than 20 minutes, and the experimental results demonstrate that the proposed self-calibration method achieves higher accuracy compared to the existing method. Furthermore, the inertial measurement unit aids in estimating the robot's pose. An error-state Kalman filter is applied to fuse the inertial measurement unit outputs with cable length measurements, providing an optimal estimation of the robot's pose and velocity. In comparison to the existing forward kinematic algorithm, the proposed method exhibits superior estimation accuracy and numerical stability. Experimental results reveal a notable 50% performance improvement over the conventional method.

Grounding rod hanging and removing robot with hand-eye self-calibration capability in substation

In this paper, a robot system for hanging and removing grounding rods is designed by integrating 3D recognition technology, hand-eye self-calibration technology, and automatic operation technology. Specifically, a novel hand-eye self-calibration algorithm is proposed that only uses common objects in the actual scene, which differs from traditional robot hand-eye calibration in that it requires a dedicated calibration board to assist with offline completion. Addressing the problem that existing self-calibration methods cannot be optimized as a whole, resulting in low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Experimental verifications are conducted using both public dataset and practical robot system. In the dataset experiment, our method demonstrates superior accuracy and robustness compared to existing self-calibration methods. Furthermore, the practical robot platform experiment confirms the feasibility and efficacy of our approach across various wind speeds and lighting conditions.

Self-Calibration for Star Sensors.

Aiming to address the chicken-and-egg problem in star identification and the intrinsic parameter determination processes of on-orbit star sensors, this study proposes an on-orbit self-calibration method for star sensors that does not depend on star identification. First, the self-calibration equations of a star sensor are derived based on the invariance of the interstar angle of a star pair between image frames, without any requirements for the true value of the interstar angle of the star pair. Then, a constant constraint of the optical path from the star spot to the center of the star sensor optical system is defined to reduce the biased estimation in self-calibration. Finally, a scaled nonlinear least square method is developed to solve the self-calibration equations, thus accelerating iteration convergence. Our simulation and analysis results show that the bias of the focal length estimation in on-orbit self-calibration with a constraint is two orders of magnitude smaller than that in on-orbit self-calibration without a constraint. In addition, it is shown that convergence can be achieved in 10 iterations when the scaled nonlinear least square method is used to solve the self-calibration equations. The calibrated intrinsic parameters obtained by the proposed method can be directly used in traditional star map identification methods.

A simultaneous non-invasive diagnosis method for flame temperature and velocity fields based on radiation self-calibration and optical flow

The measurement of flame temperature and velocity is crucial to combustion research, hence it is of great significance to develop a non-invasive diagnosis technology for the measurement of flame temperature and velocity. In this study, a single-camera simultaneous non-invasive diagnosis technology for flame temperature and velocity field was developed, based on radiation self-calibration and optical flow method. The diagnosis method was applied to two case studies, propane diffusion flame and solid surface spread flame. The reconstructed flame temperature field was compared with measuring results by thermocouples. Results show that the reconstructed temperature field can accurately describe the symmetrical distribution characteristics and the clear high-temperature front. The combustion zone reconstruction temperature was 1250–1380 K for propane diffusion flame and the maximum reconstruction temperature was 1400 K for solid spread flame. The maximum relative error between the reconstructed temperature and the measured values by thermocouples is less than 15 %. Additionally, the flow velocity field at the flame edge was also measured, which could provide a good qualitative characterization of flame kinematic.