228 results about "Calibration Error" patented technology

The invention particularly relates to a binocular vision-based three-dimensional reconstruction method, which comprises the following steps of: calibrating a binocular camera based on an improved Zhang Zhengyou calibration method to obtain internal and external parameters and a distortion coefficient of the camera; performing stereo correction on the image by using the internal and external parameters of the camera and the distortion coefficient obtained by calibration, so that the binocular image meets an epipolar constraint condition; a multi-strategy optimized semi-global stereo matching algorithm is adopted to process the image after stereo correction, and a disparity map is generated; based on the generated disparity map, generating a three-dimensional point cloud through a triangulation principle; carrying out anti-interference processing and registration optimization on the three-dimensional point cloud; and performing global splicing on the three-dimensional point clouds subjected to anti-interference processing and registration optimization based on a sequential registration error sharing strategy to complete three-dimensional reconstruction. According to the method, the key problems of large calibration error, weak texture matching failure, point cloud noise sensitivity and registration accumulative error in a traditional method are solved, and the reconstruction precision and stability are remarkably improved.

The invention discloses a monocular and binocular cooperative localization and mapping method and device for underwater refraction compensation, and the method comprises the steps: extracting checkerboard angular points, compensating an underwater light path through employing a two-time vector correction model based on a Snell law, and precisely calibrating the internal and external parameters of a binocular camera. According to the method, an underwater real light path is effectively restored, and calibration errors caused by medium refraction are remarkably reduced. Then epipolar correction is performed on the corrected left and right images, so that the geometric consistency of stereo matching is ensured; and finally, monocular and binocular poses are fused, and seven-degree-of-freedom similarity transformation global alignment is applied, so that the problem of monocular scale uncertainty is effectively solved, and high-precision and high-robustness three-dimensional mapping is realized by means of binocular real scale introduction. Key links such as camera pre-calibration, refraction compensation, image correction, monocular tracking, binocular depth measurement, scale alignment and the like are connected in series to form an integrated process, so that the underwater vision positioning and mapping precision is improved.

The invention provides an intelligent electric meter calibration method and device, computer equipment and a storage medium, and the method comprises the steps: constructing a single meter-transformer area double-layer data system through a special transformer collection terminal, and collecting transformer area total intelligent electric meter data, user meter temperature and humidity, an electric energy observation value, transformer area line impedance and load unbalance degree data; constructing an energy conservation optimization function according to the user meter electric energy observation value and the total meter electric energy by combining the deviation with the observation value; determining line loss data according to total meter current and user temperature and humidity, determining power supply stability through total meter voltage, and determining optimization function constraint conditions according to corresponding data; and solving an optimal user table software compensation coefficient and a transformer area line loss correction coefficient by a generalized genetic algorithm, updating a humidity-error correlation coefficient and a line loss reference value by a feedback performance attenuation model to determine an adjusted single table calibration error range, and performing single table calibration by combining the user table temperature and humidity and an electric energy observation value. By adopting the scheme, the accuracy, the reliability and the environmental adaptability of the calibration of the intelligent electric meter are improved.

The invention provides a chip test calibration device and method, and relates to the technical field of chip test. The chip test calibration device comprises a base, a cooling air supply device, a control device, a chip and two groups of manipulators. According to the invention, the chip is heated by setting different temperature environments in stages, and the multi-point synchronous temperature measurement of each temperature stage is combined, so that the heat distribution on the surface of the chip can be comprehensively captured, and the calibration error caused by non-uniform temperature or a detection blind area is avoided; the chip is automatically circulated among the heating station, the detection station and the cooling station, synchronous processing and continuous detection of multiple chips are achieved, the single-batch detection period is greatly shortened, the surface of the chip is directly blown by directional airflow, an efficient air return channel is matched, the temperature of the chip can be rapidly reduced after detection is completed, idle waiting for station switching is reduced, and the overall production efficiency is improved.

The invention discloses an evidence deep learning method for double-layer dynamic uncertainty calibration based on a meta-strategy. A double-layer optimization architecture is adopted. An inner layer optimizes an evidence deep learning model to execute a pixel-level segmentation task and estimate uncertainty; and the outer layer optimizes a state-aware meta-policy network. The meta-policy network receives state information reflecting training dynamics in real time and generates key hyper-parameters used for configuring an inner-layer model loss function according to the dynamic state; and a multi-target reward signal is formed through performance in multiple aspects of prediction accuracy, calibration error, misclassification uncertainty and the like of a periodic evaluation model on a verification set. According to the method, hyperparameter dynamic adaptive adjustment is carried out by introducing a state-aware meta-strategy, the limitation that a traditional method depends on static setting is overcome, prediction precision and uncertainty calibration can be better balanced, and the reliability and generalization ability of a deep learning model in high-risk application scenes (such as medical image analysis) are remarkably improved.

The invention relates to the technical field of drainage plate positioning, and discloses a pile hole positioning method and system for a drainage plate pile pipe based on a multi-source sensor. Synchronous data acquisition of a multi-source heterogeneous sensor is achieved through a GNSS receiver and an IMU, a data basis is provided for subsequent fusion calculation, and then pile pipe root coordinates and pile pipe three-dimensional attitude angles are dynamically calculated through Kalman filtering fusion according to pile driver top coordinates and pile pipe motion data. Root posture resolving errors caused by static calibration errors and structural deformation in motion are avoided; then, the coordinates of the tip end of the pile pipe are dynamically calculated through coordinate rotation transformation, machine body motion data are introduced to compensate deviation calculation, and therefore the problem that in the prior art, due to the fact that the three-dimensional posture change of the pile pipe in the construction process is neglected, drainage plate pile hole positioning is not accurate enough is solved; and it is ensured that each pile hole can meet the design precision requirement, and the problem that the construction perpendicularity is uncontrollable is fundamentally solved.

The invention provides an error modeling method and system based on a CCD camera alignment assembly system, and relates to the technical field of high-precision electronic manufacturing assembly, and the method comprises the steps: obtaining internal parameters and external parameters of a CCD camera through calibration, building a camera and mechanical coordinate system conversion relation, and constructing a collaborative calibration error tree; controlling the motion platform to move, synchronously acquiring pose and environment temperature data, decomposing vibration and thermal deformation error components through time-frequency analysis, and superposing the vibration and thermal deformation error components into dynamic errors; extracting a positioning error based on the pose data, and constructing a mechanical-optical coupling error spectrum in combination with the distortion parameters; and finally, fusing the collaborative calibration error tree, the dynamic error and the coupling error atlas to generate a global error atlas, and completing alignment assembly system error modeling based on the CCD camera. According to the invention, the precision and applicability of error modeling are improved.

The invention relates to the technical field of data error correction, in particular to a calibration error correction method and system for a falling weight deflectometer, and the method comprises the steps: obtaining deflection values of a displacement sensor of the falling weight deflectometer to form a deflection program sequence, calculating deflection abnormal values of the deflection values, obtaining deflection rightward sequences of the deflection values, and calculating deflection error sequences of the deflection values; the method comprises the following steps: acquiring a deflection value of each deflection value, further acquiring a deflection continuation characteristic value of each deflection value, acquiring a load transfer weight of each deflection value through a correlation degree between local deflection sequences of deflection values of the same bit sequence in a program sequence during adjacent deflection, and acquiring a road looseness influence degree of each deflection value in combination with the deflection continuation characteristic value corresponding to each deflection value. Further acquiring deflection vibration characteristic values of the deflection values and smooth window scales of the deflection values, and smoothing the program columns during deflection so as to correct deflection data errors of the drop weight deflectometer. According to the application, the calibration error correction precision of the drop weight deflectometer can be improved.

The invention discloses a thunder-vision fusion multi-sensor collaborative sensing system along a railway and an installation and calibration method, and belongs to the technical field of intelligent traffic and environment sensing. Aiming at the problems of low target detection precision, poor multi-source data fusion timeliness, dependence on manpower on sensor installation and calibration and the like in a complex scene along a railway, the system provides an innovative architecture of'layered sensing-dynamic fusion-autonomous calibration '. The multi-modal sensor array of a laser radar, a millimeter-wave radar, a visible light camera and a thermal infrared imager is deployed; autonomous optimization of sensor pose parameters is realized by using track geometric constraint and a deep learning model; an edge computing node and a cloud collaboration platform are integrated, and real-time target tracking, intrusion early warning and equipment state diagnosis are supported. Experiments show that the target detection accuracy rate of the system is greater than or equal to 98.5%, the false alarm rate is less than or equal to 0.3% and the self-calibration error of sensor installation parameters is less than 0.05 degree in the scenes of rain and fog, night, high-speed movement and the like, which are improved by more than 40% compared with the traditional scheme.

The invention provides a water quality parameter inversion method and system based on spectrum correction and electronic equipment, and relates to the technical field of water environment remote sensing. According to the method, the water quality parameter and the water body reflectivity value of the surface feature spectrometer are measured synchronously when the remote sensing image passes through the scene, spectrum correction is carried out on the remote sensing image by using actually measured data as a reference, and the accuracy of water quality parameter inversion is improved. Firstly, water quality parameter and water body reflectivity value measurement of a surface feature spectrometer are synchronously carried out innovatively when a remote sensing image passes through a scene, so that the consistency of measured data and the remote sensing image in time and space is ensured, and a reliable basis is provided for subsequent spectrum correction and water quality parameter inversion modeling. Secondly, a spectrum correction model is established based on the synchronously acquired measured data, the spectrum of the remote sensing image can be adjusted according to the measured data, and the reflectivity deviation caused by atmospheric correction errors and sensor calibration errors is reduced, so that the accuracy of the spectrum data is improved.

The invention provides a calibration piece and a calibration method for a multi-port waveguide antenna test system, and a test method and a test system for a multi-port waveguide antenna, in the calibration piece, a positioning structure can ensure the repeatability of each connection, which is the premise of high-precision calibration, and the calibration accuracy is greatly improved. The load unit, the offset short circuit unit and the offset load unit are provided with a plurality of waveguide ports which are the same as those of a tested piece and can be directly butted, the internal structure of the waveguide port of the load unit is an antenna, electromagnetic wave energy can be radiated out instead of being absorbed internally, and high precision and stability of the load standard are guaranteed; according to the method, the reference surface can be determined at the interface of the tested piece, the calibration error model can be directly tested on the reference surface, the accuracy is good, and the solved calibration error model contains all time-varying characteristics of the whole test link during each calibration, so that the method can be used for frequently calibrating according to requirements, and the calibration accuracy is high. Measurement errors caused by temperature drift and link loss can be handled, and the efficiency is high.

According to the embodiment of the invention, leakage characteristic data of a plurality of sensors are collected in real time; performing filtering processing on the leakage characteristic data and then determining an area where a leakage source of the vacuum equipment is located; determining the confidence coefficient of the area where the leakage source is located, determining the confidence coefficient of the position of the leakage source based on the confidence coefficient of the area where the leakage source is located, and giving an alarm when the confidence coefficient of the position of the leakage source reaches a confidence threshold value; and injecting helium to the leakage source position of the vacuum equipment in a preset time period to perform calibration test, calculating a calibration error of the leakage source position of the vacuum equipment, and when the calibration error exceeds an error threshold value, recalculating the confidence coefficient of the leakage source position and resetting the confidence coefficient threshold value. The problems of high cost, low detection precision, slow response speed, dependence on artificial experience and incapability of real-time monitoring due to long-time use of a helium mass spectrum detection method when leakage positioning detection is performed on a leakage source of vacuum equipment in the prior art are solved.

A traffic scene external-field-free continuous calibration method comprises the steps that a traffic scene image is collected, geometric prior features in the image are extracted, and the geometric prior features are used for representing points existing on a distortion curve on the traffic scene image; an error function is defined, distortion parameters are optimized based on the error function, and the distortion parameters are used for representing geometric offset generated by lens distortion in the image and comprise radial distortion parameters and tangential distortion parameters; based on the optimal distortion parameter, performing orthodontic processing on the acquired image by adopting a nonlinear optimization algorithm; ground coplanar feature points are extracted from the orthodontic image, and camera external parameters including a rotation matrix and a translation vector are solved by combining corresponding world coordinate system positions of the ground coplanar feature points; calibration error distribution is evaluated based on simulation or actual scene data, distortion model parameters or optimization strategies are dynamically adjusted, and online parameter updating is triggered when it is detected that in-place posture deviation exceeds a threshold value. The method can simplify the calibration process and improve the calibration efficiency.

The invention discloses an on-chip calibration device parasitic parameter determination and traceability method based on an inversion algorithm, and the method comprises the steps: determining eight error terms through TRL calibration which employs three calibration standard components: a straight-through calibration standard component, a reflection calibration standard component and a transmission line calibration standard component; the method comprises the following steps of: acquiring a relation between error terms in TRL calibration and SOLT calibration according to a signal flow graph, acquiring an expression for extracting a true value of a reflection coefficient of a calibration sheet through inversion according to a 12-item error correction model algorithm, substituting scattering parameters acquired by testing into an error term analytical solution of TRL calibration, solving a TRL calibration error term, and calculating the true value of the reflection coefficient of the calibration sheet according to the error term analytical solution of the TRL calibration. The reflection coefficient truth values of the open circuit and short circuit of the calibration piece and the load piece are extracted; according to the method, the extraction process of the parasitic parameters of the calibration piece is simplified, error sources introduced in the extraction process of the parasitic parameters of the calibration piece are reduced, and the extracted parasitic parameters of the calibration piece have a good effect.

The invention provides a calibration method, a calibration device, a tracking type scanning system and a storage medium, and the method comprises the steps: scanning a reference object on the calibration device through a scanner, and obtaining first scanning data corresponding to the reference object in a scanning coordinate system; acquiring the pose of the marker on the scanner and the pose of the calibration device by using a tracker; calibrating equipment parameters of the scanner based on the true value corresponding to the reference object and the first scanning data; based on the pose of the marker and the pose of the calibration device, converting the first scanning data to a tracking coordinate system of a tracker; and based on the true value corresponding to the reference object and the first scanning data under the tracking coordinate system, completing rigid body transformation relation calibration between the local coordinate system corresponding to the marker and the scanning coordinate system. Through the method, calibration errors can be reduced to improve measurement precision.

The invention discloses a pre-distortion calibration method and system based on a single chip microcomputer, a medium and a product, and relates to the field of radio frequency front ends. The method comprises the following steps: extracting an instantaneous amplitude value of an input signal sequence to obtain a first envelope to represent instantaneous nonlinearity; the periodic average power of the input signal is calculated and filtered through timer interruption, and a second envelope representing the heat effect is obtained. According to the method, the first envelope and the second envelope serve as double indexes, the preset two-dimensional lookup table is inquired, so that the pre-distortion calibration coefficient capable of being matched with the instantaneous nonlinearity and the thermal memory effect of the power amplifier at the same time is determined, and the input signal is calibrated. In addition, a feedback signal output by the power amplifier is obtained to calculate a distortion calibration error, and the two-dimensional lookup table is adaptively updated according to the error. According to the scheme, a composite calibration model is constructed on a low-cost single-chip microcomputer platform, and the accuracy and stability of nonlinear pre-distortion calibration of the power amplifier are guaranteed.

The invention relates to a multi-sensor synchronous calibration jig and method. The precision, efficiency and stability of multi-sensor calibration can be improved. During specific operation, firstly, the multi-sensor array is accurately installed on the jig intelligent self-adaptive clamp and connected with an interface, and a system is electrified for self-inspection and uploads data information; then, starting a multi-dimensional state sensing network to collect environmental parameters, detecting the in-place state of the sensor and obtaining a self-inspection signal, and performing initial state evaluation; predicting the state of the actuating mechanism before action in state calibration and iteratively adjusting action parameters; finally, an effective parameter data point set and metadata thereof are input, traditional calibration parameter calculation is carried out, and state metadata serves as a covariable to be input into the error compensation model; according to the method, the accuracy, stability and reliability of sensor calibration are effectively improved through comprehensive evaluation of the initial state, dynamic adjustment of parameters, timely processing of abnormal data and the like, the calibration error is reduced, and the subsequent performance of the sensor is guaranteed.

The embodiment of the invention discloses a camera group calibration method and device, equipment and a medium, and relates to the technical field of camera calibration. The method comprises the following steps: acquiring a calibration object image of a to-be-calibrated object in a candidate camera coordinate system, and determining candidate calibration object coordinates in the candidate camera coordinate system according to the calibration object image; judging whether the candidate camera coordinate system is a reference camera coordinate system or not, and if not, converting the candidate calibration object coordinates under the candidate camera coordinate system into reference calibration object coordinates under the reference camera coordinate system according to a preset camera group transformation matrix; according to the reference calibration object coordinate and a preset calibration error function, determining corresponding calibration compensation data, and according to the reference calibration object coordinate and the calibration compensation data, determining a target calibration object coordinate of the object to be calibrated; wherein the calibration error function is determined based on sphere images of the calibration sphere at different acquisition positions in the view field overlapping area of the camera group and a preset sphere radius. And the space consistency and robustness of camera group calibration are improved.

The invention provides an automatic trapezoid correction method and system for a projector and a medium. The method comprises the following steps: acquiring attitude information of the projector and spatial information of the projector and a projection surface; under the condition that optical emission parameters of the projector are known, a projection geometric model is established based on the attitude information and the space information, and transformation parameters used for projector trapezoidal correction are generated accordingly. And controlling the projector to execute a trapezoidal correction action based on the transformation parameter. And controlling the projector to project a preset calibration pattern, acquiring an image of the calibration pattern by adopting the image acquisition device after the trapezoidal correction action is executed, extracting image features, and acquiring geometric parameters corresponding to the actual projection picture based on the image features. And determining a calibration error based on the difference between the geometric parameter and the transformation parameter, and generating a compensation parameter according to the calibration error. And writing the compensation parameter into the projector so as to compensate the transformation parameter when the projector performs trapezoidal correction. Through the steps, the projector generates a corresponding correction coefficient.

The invention relates to a structured light three-dimensional measurement technology, and provides a system joint calibration optimization method fusing three-dimensional offset modeling and phase identity constraint for an industrial field low-precision target scene. The method comprises the following core steps: introducing a three-dimensional offset for a target feature point, and compensating spatial deformation and eliminating scale ambiguity through zero-mean constraint; sub-pixel matching is realized based on a phase identity principle, and projection constraint is enhanced to reduce dependence on a target; and constructing a joint reprojection error function, and adjusting and synchronously optimizing system parameters and offset by means of a bundle method. The prior art depends on a high-precision plane target, and when the target has manufacturing errors, installation warping or pasting deformation, calibration errors can be transmitted through an imaging link, so that the measurement precision is reduced, and target deformation and geometric errors of a projector are difficult to compensate at the same time. The method breaks through the limitation of a traditional inverse camera model, does not need to depend on a high-precision glass target, reduces the hardware cost, and constructs a cooperative compensation mechanism of a target manufacturing error and an imaging parameter. Experiments show that after explicit compensation is carried out on the local deformation of the target by adopting the three-dimensional offset factor, the flatness error gt is detected; on a paper target of 10 microns, the re-projection error of the camera is reduced by 79.6%, and the re-projection error of the projector is reduced by 74.8%; sub-pixel matching is achieved through phase identity constraint, multi-parameter collaborative optimization of bundle adjustment is combined, the high-precision measurement requirement of an industrial site is met, and an efficient solution is provided for high-precision three-dimensional measurement.

The invention discloses a high-precision bridge structure global strain measurement method and system, and relates to the technical field of civil engineering-structure health monitoring. The system comprises a video acquisition module which is deployed at a stable observation point of a bridge and acquires a standard frame rate video; the pixel domain preprocessing module constructs a space-time matrix and generates a weight matrix and a shielding mask; the multi-scale spatial adjacency graph module divides nodes according to first-level component-second-level segmentation-superpixel clustering, defines an edge weight and applies geometric prior constraints; the variation displacement solving module obtains a displacement field through a multi-constraint objective function and ADMM iteration; the adaptive baseline strain calculation module dynamically selects the baseline length and calculates the strain; the abnormal point processing module ensures data integrity through a discrimination-elimination-re-estimation process; and the cross-section synchronous constraint module reduces displacement deviation. The method solves the problems of large traditional calibration error, local measurement limitation and environmental interference, and guarantees the reliability of bridge health monitoring data.

The invention provides a high-fidelity state observation visual positioning system and method for a high-vibration platform, belongs to the field of machine vision and image processing, and is suitable for external closed-loop control of high-vibration platforms such as an unmanned aerial vehicle with arms. In order to solve the problems of measurement failure, large data noise and multi-sensor calibration errors caused by motion blur in the prior art, a single RGBD camera (a view field covers an external target and a mechanical arm visual label) is adopted, and a process of locking a region of interest by deep learning-measuring a pose by a high-precision algorithm-multi-label weighted fusion-time domain low-pass smoothing is combined. And outputting a high-fidelity pose signal. According to the method, multi-sensor calibration errors are eradicated, the anti-fuzzy capacity is high, signals are smooth to meet the control requirement, the positioning precision of the mechanical arm can be improved to the 3 mm level, and the dynamic task success rate is remarkably increased.

The invention relates to the technical field of image recognition, and provides a vehicle virtual model generation method based on a large model, which comprises the following steps: acquiring multi-modal observation data, inputting a pre-trained semantic and instance segmentation model, projecting a two-dimensional label to a point cloud, and performing confidence weighted fusion on a multi-view label to obtain a multi-modal observation model; performing statistics and correlation analysis on the processed data; candidate fields are extracted, calibration is carried out on a verification set, field credibility is calculated based on calibration errors, field sub-vectors are combined into an overall condition vector, then estimation and processing are carried out, and the overall condition vector serves as input to construct a conditional multi-modal generator; inputting the condition vector into a discriminator to form an adversarial training framework, jointly constructing adversarial loss by adopting a stabilized adversarial target and a regularization measure, and optimizing a generative model; and the generator output is packaged into a simulation loadable package, batch playback is carried out by an automatic script, and closed-loop correction is implemented by an automatic optimization algorithm.

The invention discloses an automatic classification method for hepatic echinococcosis based on an ultrasonic image. The method comprises the following steps: in a preprocessing stage, denoising, normalization and size standardization are carried out on an original image, and quality gating is executed; in the segmentation stage, focus positioning and pixel-level segmentation are completed on the standardized image, and a stable mask is obtained through'opening-closing 'morphology and coverage rate constraint; in the feature and initial judgment stage, multi-scale feature extraction is carried out on a focus area, and uncertainty is calculated; in the fine judgment stage, features are dynamically weighted and fused according to reliability, and focus types and activity grades are output; in the closed-loop stage, self-adaptive write-back of the model parameters, the segmentation threshold and the enhancement gain is driven by the compound loss; in the result stage, temperature scaling and expected calibration error evaluation are implemented, and a structured report containing type, classification, confidence and parameter snapshots is generated.

The invention relates to a self-powered detector on-pile test device and method.The device comprises an outer support fixed in a test dry hole channel and an inner support coaxially nested in an inner cavity of the outer support, the outer diameter of the outer support and the inner diameter of the test dry hole channel form micro-tight fit, and the inner support can rotate around the axis relative to the outer support; a first mounting position for mounting at least one standard self-powered detector is arranged on the outer support, a second mounting position for mounting at least one self-powered detector to be detected is arranged on the inner support, and a cross arm is arranged at the bottom of the outer support and used for being in contact with the lower end of the inner support so as to limit the axial insertion position of the inner support. According to the on-pile test device and method for the self-powered detector, single-point calibration errors caused by uneven radial distribution of neutron fluence rates can be effectively eliminated or greatly reduced, and the calibration precision and the representativeness of results are remarkably improved.

The invention discloses a modal force hammer dynamics test modal parameter error calibration method, and relates to the technical field of error calibration, and the method comprises the steps: obtaining a collision angle interval based on a pendulum bob type calibration device; using a multi-impact analysis method to obtain an impact correlation curved surface of the collision angle interval; performing error calibration on the obtained modal parameters based on the impact correlation curved surface and the release angle of the modal force hammer; the method is used for solving the problems that in an existing modal force hammer dynamics test modal parameter error calibration method, springback buffering after impact of a soft rubber hammer head and a pendulum bob cannot be accurately controlled, impact data are discrete in multiple times of calibration, modal parameters of the rubber hammer head after impact of the pendulum bob cannot be effectively obtained, calibration precision is affected, and the calibration precision is influenced. And the parameter calibration error is improved.

The application discloses a kind of methods for multi-pendulum angle calibration mistake-proofing, belong to the technical field of inspection and detection, it is characterized in that, comprising the following steps: S1, determine the distribution of each angle of standard ball measuring point;S2, standard ball is fixed on machine tool;S3, obtain standard ball ball center coordinates O';S4, contact probe is installed to machine tool spindle;S5, carry out multi-pendulum angle calibration, and obtain the calibration compensation value r of different angles;S6, calculate the difference δ between each other of multi-pendulum angle compensation value, according to the correctness of the correctness of calibration result if the difference δ is judged measurement part tolerance, if difference δ <1 / 4 product tolerance, then calibration data is correct;S7, if difference δ >1 / 4 product tolerance, then repeat S3-S6 steps.The application directly utilizes the calibration result under multi-pendulum angle to verify each other, both carry out mistake-proofing verification, and do not increase additional time factor, can effectively guarantee the accuracy of calibration result, improve the measurement accuracy and measurement efficiency of on-line measurement.

The invention provides a system and method for calibrating a pipeline flow measuring instrument, and the method comprises the steps: calculating difference index values between channels according to a flow distribution feature vector, carrying out the grouping of the difference index values through a clustering analysis method, and determining a channel set with unbalanced flow distribution for subsequent abnormality analysis; acquiring flow data of an abnormal channel from the abnormity identification result, and correcting the flow data by adopting a self-adaptive filtering method to obtain a corrected flow distribution diagram for recovering the balance state of the pipeline system; and recalculating an overall calibration parameter according to the corrected flow distribution diagram, adjusting the calibration parameter value through an iterative optimization method, judging whether a calibration error tends to be stable or not, and obtaining a final pipeline flow calibration model.

The invention relates to an error calibration bird's-eye view perception method and system. The method comprises the following steps: extracting image features from data acquired from a first modal sensor, extracting geometric height prior features from data acquired from a second modal sensor, and generating height prior information; performing geometric calibration on the image features based on the information to generate calibrated image features; and performing iterative bidirectional alignment and fusion on the calibrated image features and the geometric height prior features to generate fused aerial view features, and executing a three-dimensional target detection task based on the fused aerial view features. According to the method, laser radar geometric priori is introduced, and cross-modal geometric calibration is realized in combination with deformable attention and a multi-scale deformation field, so that the robustness of height estimation and the alignment quality of multi-modal features in an aerial view space are effectively improved, and the sensing precision and stability of roadside three-dimensional target detection under a calibration error condition are remarkably enhanced.

The application discloses a mechanical hand calibration and space angle measurement method suitable for a tilt camera and a robot, and comprises the following steps: controlling a mechanical hand end to move along a preset calibration plane to different positions for multiple times, recording the mechanical hand end coordinates after each movement, and identifying and recording the pixel coordinates of all calibration points on a calibration plate image shot by a camera through an image processing algorithm. According to the mechanical hand end coordinates and the pixel coordinates of all calibration points on the calibration plate, a projection transformation matrix between the mechanical hand end coordinates and the pixel coordinates of the center points of all calibration points on the calibration plate is calculated by using a least square method. The camera intrinsic parameter matrix is applied to decompose each projection transformation matrix, and the space angle between the camera imaging plane and the mechanical hand end movement plane, or the space angle between the camera imaging plane, the mechanical hand end movement plane and the calibration plate plane is calculated. The calibration process of the application is simple, the result is high in precision, and the calibration error caused by the camera installation angle can be eliminated.