Online Calibration Method Research Articles

Online Pyrometry Calibration for Industrial Combustion Process Monitoring

Temperature and its distribution are crucial for combustion monitoring and control. For this application, digital camera-based pyrometers become increasingly popular, due to its relatively low cost. However, these pyrometers are not universally applicable due to the dependence of calibration. Compared with pyrometers, monitoring cameras exist in all most every combustion chamber. Although these cameras, theologically, have the ability to measure temperature, due to lack of calibration they are only used for visualization to support the decisions of operators. Almost all existing calibration methods are laboratory-based, and hence cannot calibrate a camera in operation. This paper proposes an online calibration method. It uses a pre-calibrated camera as a standard pyrometer to calibrate another camera in operation. The calibration is based on a photo taken by the pyrometry-camera at a position close to the camera in operation. Since the calibration does not affect the use of the camera in operation, it sharply reduces the cost and difficulty of pyrometer calibration. In this paper, a procedure of online calibration is proposed, and the advice about how to set camera parameters is given. Besides, the radio pyrometry is revised for a wider temperature range. The online calibration algorithm is developed based on two assumptions for images of the same flame taken in proximity: (1) there are common regions between the two images taken at close position; (2) there are some constant characteristic temperatures between the two-dimensional temperature distributions of the same flame taken from different angles. And those two assumptions are verified in a real industrial plants. Based on these two verified features, a temperature distribution matching algorithm is developed to calibrate pyrometers online. This method was tested and validated in an industrial-scale municipal solid waste incinerator. The accuracy of the calibrated pyrometer is sufficient for flame monitoring and control.

Online Self-Calibration of 3D Measurement Sensors Using a Voxel-Based Network.

Multi-sensor fusion is important in the field of autonomous driving. A basic prerequisite for multi-sensor fusion is calibration between sensors. Such calibrations must be accurate and need to be performed online. Traditional calibration methods have strict rules. In contrast, the latest online calibration methods based on convolutional neural networks (CNNs) have gone beyond the limits of the conventional methods. We propose a novel algorithm for online self-calibration between sensors using voxels and three-dimensional (3D) convolution kernels. The proposed approach has the following features: (1) it is intended for calibration between sensors that measure 3D space; (2) the proposed network is capable of end-to-end learning; (3) the input 3D point cloud is converted to voxel information; (4) it uses five networks that process voxel information, and it improves calibration accuracy through iterative refinement of the output of the five networks and temporal filtering. We use the KITTI and Oxford datasets to evaluate the calibration performance of the proposed method. The proposed method achieves a rotation error of less than 0.1° and a translation error of less than 1 cm on both the KITTI and Oxford datasets.

Calibration method of ultra-low emission particulate concentration measurement by light scattering method

Abstract To reduce the error caused by optical device pollution and background value drift in the measurement process of ultra-low emission particulate concentration by light scattering method, an on-line calibration method which can realize self-check and full-range calibration was put forward. The single light source spectral method was used to set up calibration light path, through a strong emphasis on technology achieving light signal extraction. The calibration effect was evaluated based on the dust tunnel system. The results show that the light intensity-modulated technique can significantly improve the measurement accuracy of low concentration particles compared with the constant light intensity technology. When the concentration of particles to be measured is higher than 500 μg/m³, the relative error between the measurement results of the experimental prototype and those of the filter membrane weighing method is less than 10 %. The optimal calibration period of the measuring instrument is 3 hours. When the variation deviation from the calibration light intensity of light intensity modulation is less than 2 %, the relative error between the particle concentration measurement result and the filter membrane weighing method is less than 10 %.

Online calibration technology for a one-dimensional laser Doppler velocimeter based on a strapdown inertial navigation system.

In the integrated navigation system, which consists of a laser Doppler velocimeter (LDV) and strapdown inertial navigation system, it is necessary to calibrate the LDV. We propose an online calibration method for a LDV based on the strapdown inertial navigation system. This system uses highly accurate output information in the early stage of the inertial navigation system to carry out coarse calibration for the velocimeter and then employs a Kalman filter to correct the results of coarse calibration to achieve high precision calibration for the velocimeter. Experimental results show that the relative distance error of dead reckoning is less than 0.02%, indicating that the proposed online calibration method can achieve high-precision calibration for the velocimeter.

A high-efficiency and new online calibration method in CD-CAT based on information gain of entropy and EM algorithm

A high-efficiency and new online calibration method in CD-CAT based on information gain of entropy and EM algorithm

Free trajectory cone beam computed tomography reconstruction method for synchronous scanning of geometric calibration phantom and imaging object

In order to suppress the geometrical artifacts caused by random jitter in ray source scanning, and to achieve flexible ray source scanning trajectory and meet the requirements of task-driven scanning imaging, a method of free trajectory cone-beam computed tomography (CBCT) reconstruction is proposed in this paper. This method proposed a geometric calibration method of two-dimensional plane. Based on this method, the geometric calibration phantom and the imaging object could be simultaneously imaged. Then, the geometric parameters could be obtained by online calibration method, and then combined with the geometric parameters, the alternating direction multiplier method (ADMM) was used for image iterative reconstruction. Experimental results showed that this method obtained high quality reconstruction image with high contrast and clear feature edge. The root mean square errors (RMSE) of the simulation results were rather small, and the structural similarity (SSIM) values were all above 0.99. The experimental results showed that it had lower image information entropy (IE) and higher contrast noise ratio (CNR). This method provides some practical value for CBCT to realize trajectory freedom and obtain high quality reconstructed image.

Research on innovative one-dimensional mathematical model applied to the on-line measurement for transient discharge coefficient of diesel injector nozzle

This paper presents an innovative one-dimensional mathematical model applied to the on-line measurement for transient discharge coefficient of diesel injector nozzle. The state change in the fuel system is abstracted into the transfer and evolution process of the Riemann wave, and a direct mathematical relationship between the pressure and the transient discharge coefficient in a single Riemann wave environment is established. The on-line calibration method of fuel sound velocity is proposed, which avoids the complicated off-line calibration of fuel physical property. The time-domain characteristics recognition method of input signal is put forward and the model is segmented according to time characteristics to improve the applicability. The decoupling algorithm of reflected wave and the correction method of interference wave are proposed to improve the model robustness. The experimental results show that this mathematical model has good accuracy. It is found that the variation of the average discharge coefficient with injection pressure and pulse width is nonlinear due to the cavitation and needle valve average opening.

A real-time low-computation cost human-following framework in outdoor environment for legged robots

Legged robots have potential advantages in mobility compared with wheeled robots. Hence, legged robots are widely utilized in outdoor unstructured environments. Human-following operation is one of important tasks for outdoor robots. However, most current human-following strategies requires large amount of computation resource hence difficult to be applied to legged robots. This paper proposes real-time low-computation cost human-following framework in outdoor environment for legged robots. Our method takes a full consideration of the differences between legged robots and wheeled robots. Firstly, an on-line extrinsic calibration method is proposed to calculate the camera coordinate and the world coordinate system. Then, a real-time low-computation cost human-following method utilizing RGBD cameras and 3D LIDAR is proposed. The robot motion considers tracking the leading person while avoiding obstacles. Furthermore, a dynamic alternating tripod trotting gait is developed to control the robot to follow the leading person. Finally, the method is implemented and tested on a hexapod robot Qingzhui with indoor and outdoor experiments. The framework proposed in this paper can be a valuable reference for other legged robots when operated in outdoor environments.

A review of 28 free animal-tracking software applications: current features and limitations.

Well-quantified laboratory studies can provide a fundamental understanding of animal behavior in ecology, ethology and ecotoxicology research. These types of studies require observation and tracking of each animal in well-controlled and defined arenas, often for long timescales. Thus, these experiments produce long time series and a vast amount of data that require the use of software applications to automate the analysis and reduce manual annotation. In this review, we examine 28 free software applications for animal tracking to guide researchers in selecting the software that might best suit a particular experiment. We also review the algorithms in the tracking pipeline of the applications, explain how specific techniques can fit different experiments, and finally, expose each approach's weaknesses and strengths. Our in-depth review includes last update, type of platform, user-friendliness, off- or online video acquisition, calibration method, background subtraction and segmentation method, species, multiple arenas, multiple animals, identity preservation, manual identity correction, data analysis and extra features. We found, for example, that out of 28 programs, only 3 include a calibration algorithm to reduce image distortion and perspective problems that affect accuracy and can result in substantial errors when analyzing trajectories and extracting mobility or explored distance. In addition, only 4 programs can directly export in-depth tracking and analysis metrics, only 5 are suited for tracking multiple unmarked animals for more than a few seconds and only 11 have been updated in the period 2019-2021.

Passive‐compensation clamp‐on two‐stage current transformer for online calibration

Digital measurement equipment such as electronic transformers enable real-time functional control and monitoring systems to be built in power grids to accommodate the large-scale access of distributed renewable energy to the grid. However, the current offline calibration of electronic transformers is difficult to implement; therefore, an online calibration method for electronic transformers must be developed to ensure their accuracy. The design of a clamp-on two-stage current transformer (CTS-CT) with a passive compensation loop is presented herein. The compensation loop is utilised to reduce both the amplitude and phase errors, thereby achieving high accuracy. The formulas for calculating the resistance and capacitance of the passive compensation loop are presented. The expanded uncertainties (95% confidence level) of the proposed CTS-CT are estimated to be lower 0.03% for the amplitude error and 1′ for the phase error. Experimental results indicate that the proposed CTS-CT prototype is not affected by the external magnetic field nor the opening and closing times, thereby enabling good-quality online calibration for field applications. Moreover, the proposed CTS-CT adopts neither an active compensatory circuit nor software compensation; therefore, the manufacturing of the CTS-CT is steady and simple, and the calibration procedures are simplified.

Online ultrasonic nebulizer assisted laser induced breakdown spectroscopy (OUN-LIBS): An online metal elements sensor for marine water analysis

Metal element detection technologies for seawater analysis are very important in the fields of marine scientific investigation and environmental monitoring. The current technologies cannot meet the requirements of detection capabilities, such as online, real-time, multi-element and highly sensitive while used in the above fields on-site. A newly developed sensor named online ultrasonic nebulizer assisted laser induced breakdown spectroscopy (OUN-LIBS) is introduced in this paper which is expected to be used for on-site seawater analysis. The core design idea of this sensor is to combine an online ultrasonic nebulizing auxiliary device with a traditional LIBS system to nebulize the liquid samples into aerosol for LIBS detection in order to realize a continuously, highly sensitive, stable and fast on-site detection. Based on this idea, a hardware system composed of an adapter module, a nebulizing module and an optical module and a software system with communication, control and display functions were designed. The laboratory experiments and marine field testing were carried out to assess the sensor. Under the status of continuous online detection, the LODs of the 6 target elements are lower than 1 ppm and the stability of spectral line intensity of Ca is lower than 0.38%. Besides the traditional calibration curve method, an online and on-site standard sample calibration method is adopted in the concentration quantitative analysis. The results of research show that the accuracy of online quantification analysis is very impressive even in the marine field testing.

An Online Calibration Method Based on n-Tuple and Opportunistic Communication for Mine Mass Portable Gas Sensors.

Due to the increasing deployment of the Internet of Things (IoT) in the mining industry, portable gas monitoring devices have been widely used. Sensor calibration of large-scale portable gas monitoring devices is becoming an urgent problem to be solved. An online sensor calibration algorithm based on n-tuple and opportunistic communication is proposed based on the specific characteristics (i.e., ‘single-sensor, multi-position’ and ‘multi-sensor, single-position’) of each portable gas monitoring device employed. In this paper, data collected from portable and fixed sensors were defined as multi-dimensional data points and gas monitoring data pairs, respectively. The cluster-based self-adaptive weighted data fusion algorithm and multi-period single sensor reliability fusion algorithm were proposed and used for overall judging. The overall judgments were broadcast to each wireless access point by network, and the reliability of the calibration information transmission was enhanced by opportunistic communications. The simulation results revealed that efforts required for the calibration of portable sensors were reduced significantly, and their reliability was improved.

Real-Time Wind Field Estimation and Pitot Tube Calibration Using an Extended Kalman Filter

The airspeed is an important feedback signal for flight control, and its measurement accuracy is related to the safety of aircraft, especially for hybrid vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAV) in the transition phase. However, offline calibration of the pitot tube cannot fully simulate the situation in real cases, and this is why online calibration after installation is necessary. In addition, the environmental wind field creates a high risk for the conversion flight of a hybrid UAV, thus real-time wind field measurement of the flight field has great significance for the flight path planning of the conversion process. In this article, an innovative method for online calibration of pitot tube and wind field estimation is proposed. After establishing the extended Kalman filter (EKF) for estimation, an analysis method is proposed to analyze the observability of EKF under different flight strategies. Then, the hovering flight is selected as the experimental flight trajectory. The laboratory computer simulation and flight experiment results validate the theory and prove that the proposed method could re-calibrate the scale factor of the pitot tube and estimate the wind field.

On-line self-calibration method for unattended manipulators based on Gaussian motion model and visual system

PurposeFor some special manipulators such as the ones work at the space station, nuclear or some other unmanned environments, the overload, collision, vibration, temperature change or release of the internal stress would affect the structural parameters. And thus the operation precision might constantly decrease in long-term use. In these unmanned environments, the unattended manipulators should calibrate itself when they execute high precision operations or proceed self-maintenances. The purpose of this paper is to propose an automatic visual assistant on-line calibration (AVOC) method based on multi-markers.Design/methodology/approachA camera fixed on the end of the manipulator is used to measure one to three identification points, which forms an unstable multi-sensor eye-in-hand system. A Gaussian motion method which combines the linear quadratic regulator control and extended Kalman filter together is proposed to make the manipulator track the planned trajectories when its inaccurate structural parameters form uncertain motion errors. And a Monte-Carlo method is proposed to form a high precision and stable signal acquisition when the visual system has measurement errors and intermittent signal feedback. An automatic sampling process is adopted to select the optimal measurement points basing on their variances.FindingsData analysis and experiment results prove the efficiency and feasibility of the method proposed in this paper. With this method, the positioning accuracy is largely promoted from about 2 mm to 0.04–0.05 mm.Originality/valueExperiments were carried out successfully on a manipulator in a life sciences glove box that will work at the Chinese space station. It is a low cost and efficient manipulator calibration method. The whole autonomic calibration process takes less than 10 min and requires no human intervention. In addition, this method not only can be used in the calibration of other unmanned articulated manipulator that works in deep ocean, nuclear industry or space but also be useful for the maintenance work in modern factories owing a lot of industrial robots.

Research on online calibration of lidar and camera for intelligent connected vehicles based on depth-edge matching

Abstract The practicality of online calibration algorithms in actual autonomous driving scenarios is enhanced by proposing an online calibration method for intelligent networked automotive lidar and camera based on depth-edge matching. The initial values of external parameters are estimated and calculated through hand-eye calibration. The solution of hand-eye calibration is optimized and accurate external parameters are obtained through data conversion. The CMA-ES algorithm is utilized to optimize the optimized parameters which are further compared with the conventional method based on edge matching. It is found that the provided frames of data, the external parameters can be appropriately improved by the method in this paper, and the algorithm congregates in about 1000 seconds. However, the conventional method cannot optimize the parameters correctly when there are only 2 frames of data. The rotation error of most results of this method is between 0.1° and 0.8°, and the translation error is between 0.02m and 0.06m. Compared with other representative algorithms of various methods, the errors in all aspects are more balanced and there is no outstanding error value.

RGGNet: Tolerance Aware LiDAR-Camera Online Calibration With Geometric Deep Learning and Generative Model

Accurate LiDAR-camera online calibration is critical for modern autonomous vehicles and robot platforms. Dominant methods heavily rely on hand-crafted features, which are not scalable in practice. With the increasing popularity of deep learning (DL), a few recent efforts have demonstrated the advantages of DL for feature extraction on this task. However, their reported performances are not sufficiently satisfying yet. We believe one improvement can be the problem formulation with proper consideration of the underneath geometry. Besides, existing online calibration methods focus on optimizing the calibration error while overlooking the tolerance within the error bounds. To address the research gap, a DL-based LiDAR-camera calibration method, named as the RGGNet, is proposed by considering the Riemannian geometry and utilizing a deep generative model to learn an implicit tolerance model. When evaluated on KITTI benchmark datasets, the proposed method demonstrates superior performances to the state-of-the-art DL-based methods. The code will be publicly available at https://github.com/KleinYuan/RGGNet .

Online multi-sensor calibration based on moving object tracking

Modern autonomous systems often fuse information from many different sensors to enhance their perception capabilities. For successful fusion, sensor calibration is necessary, while performing it online is crucial for long-term reliability. Contrary to currently common online approach of using ego-motion estimation, we propose an online calibration method based on detection and tracking of moving objects. Our motivation comes from the practical perspective that many perception sensors of an autonomous system are part of the pipeline for detection and tracking of moving objects. Thus, by using information already present in the system, our method provides resource inexpensive solution for the long-term reliability of the system. The method consists of a calibration-agnostic track to track association, computationally lightweight decalibration detection, and a graph-based rotation calibration. We tested the proposed method on a real-world dataset involving radar, lidar and camera sensors where it was able to detect decalibration after several seconds, while estimating rotation with error from a 20 s long scenario.

On-line real-time mistuning identification and model calibration method for rotating blisks based on blade tip timing (BTT)

Identifying the mistuning parameters and calibrating the finite element model (FEM) of the mistuned blisk in real-time is a meaningful way to acquire the blisk’s dynamic characteristics and predict its stress response. Because of the wearing and other reasons, the blisk’s mistuning is changing during the operation. Hence a BTT based rotating-blisk mistuning identification and model calibration (BR-IDC) method in real-time has become a necessity. The rotating effects (stiffening, softening and Coriolis force) are considered in the BR-IDC. The synergistic effect of the mass and stiffness mistuning is analyzed, and the effect is presented by a standard mistuning coefficient. Based on the BR-IDC, the on-line real-time mistuning identification for the rotating blisk is realized. Furthermore, the FEM was calibrated by the identification results. The BR-IDC approach is verified by FEM simulation and experimental work. The precision of the FEM is improved through the calibration.

Design and online calibration methods of pressure-independent intelligent regulating valve based on hydrodynamic resistance characteristics

In the district heating system, hydraulic maladjustment led to uneven heating and severe energy consumption. The existing valves cannot be accurately adjusted in the pipe networks and the complex structures of some valves weaken the energy-saving potential, failing to meet the demand of smart heating development. Considering these problems, the hydrodynamic resistance characteristics of regulating valves are studied, and the design and online calibration methods of the pressure-independent intelligent valve based on it are proposed. Among them, a) A hybrid optimization algorithm combining the particle swarm optimization algorithm and Elman neural network trained by Levenberg-Marquardt algorithm in identifying hydrodynamic resistance characteristics of regulating valves quickly and accurately with the least amount of experimental data is studied. b) For valves of the same type with different sizes, similitude principle provides a theoretical basis in expanding hydrodynamic resistance characteristics. c) An online calibration method of the pressure-independent intelligent regulating valve applied in the heating system based on the principles of room temperature prediction and mass conservation is proposed which avoids the inconvenience of field calibration. The pressure-independent intelligent regulating valve is worth researching and developing which provides a novel idea for decoupling the hydraulic conditions in parallel loops and reducing energy consumption.

A Hough-Space-Based Automatic Online Calibration Method for a Side-Rear-View Monitoring System.

We propose an automatic camera calibration method for a side-rear-view monitoring system in natural driving environments. The proposed method assumes that the camera is always located near the surface of the vehicle so that it always shoots a part of the vehicle. This method utilizes photographed vehicle information because the captured vehicle always appears stationary in the image, regardless of the surrounding environment. The proposed algorithm detects the vehicle from the image and computes the similarity score between the detected vehicle and the previously stored vehicle model. Conventional online calibration methods use additional equipment or operate only in specific driving environments. On the contrary, the proposed method is advantageous because it can automatically calibrate camera-based monitoring systems in any driving environment without using additional equipment. The calibration range of the automatic calibration method was verified through simulations and evaluated both quantitatively and qualitatively through actual driving experiments.