19 results about "Calibration matrix" patented technology

The application discloses an endoscope image electronic staining method, device, system and storage medium. The method extends an original wideband RGB image into a P-dimensional wideband image, and for each pixel point of a current frame: based on a current image block, a reference image block set is obtained by searching for reference image blocks in the current frame and K frames before and after the current frame; according to the similarity distance between each reference image block and the current image block, the image attenuation weight of each image block is determined, the image blocks are fused and weighted, and the RGB light reflectivity estimation data of the pixel point is obtained; based on the calibration matrix of the wideband image and the narrowband image, the narrowband light image signal data corresponding to the estimation data is obtained; based on the narrowband light image signal data of each pixel point, a narrowband image of the current frame is generated, and an electronic staining image is synthesized based on the narrowband image. The approximate imaging result of narrowband light illumination can be realized through software, so that better lesion observation effect is obtained at low cost.

Methods and systems for wavelength standardization. In some implementations, a method for standardizing wavelength measurements taken from a multi-channel detector may comprise obtaining an idealized matrix associated with a detector, such as a blood analyte detector. The idealized matrix may comprise fixed data corresponding to a fictional, ideal version of the detector. A calibration matrix associated with the detector may be obtained from measurement data taken from the detector. A pseudoinverse of the calibration matrix may be taken and the idealized matrix may be multiplied by the pseudoinverse of the calibration matrix to obtain a wavelength standardization matrix. Multi-channel spectral signal data, such as data from a plethysmography signal, may be received from the detector. A vector may be defined using the signal data and the vector may be updated using the wavelength standardization matrix.

This invention proposes a 3D modeling system and method based on force detection feedback. The 3D modeling system includes: a calibration guide tube, a 3D calibration system, a force-sensing actuator arm, a controller, a workstation computer, and main control system software. The calibration guide tube is adapted to be fixed to the force-sensing actuator arm, which has a motor power module and a measurement module. The measurement module is used to measure the operating parameters of the motor power module. The main control system software is installed on and connected to the workstation computer and stores a calibration matrix. The calibration matrix records the correspondence between the contact force of the calibration guide tube and the operating parameters. During the 3D modeling process, the workstation computer receives the operating parameters measured by the measurement module and obtains the contact force of the calibration guide tube by querying and comparing with the calibration matrix. This invention can achieve force detection using existing high-density calibration guide tubes, significantly reducing cost and system complexity. The method of inferring the force at the guide tube tip from the operating parameters is accurate and has a fast response speed.

The present application relates to the technical field of thickness measurement, and discloses a linear dimensional measurement method for AR module thickness, comprising: obtaining a spectral interference signal and resampling to a wave number space to generate a wave number domain complex sequence; extracting a real part, an imaginary part and a first derivative term of the wave number domain complex sequence; establishing an orthogonal component set; extracting a transient phase slope by using differential mapping between the orthogonal components; determining a wave number domain group delay distribution; calling a dispersion calibration matrix to correct nonlinear deviation; converting nonlinear phase evolution into linear phase gradient; fitting the linear phase gradient to solve a physical thickness value; and bypassing arctangent calculation and phase unwrapping by establishing phase gradient mapping logic, suppressing phase jump caused by interference envelope aliasing from the dimension of physical energy distribution, improving thickness calculation stability of an extremely thin medium layer under environmental interference, and realizing physical decoupling of material dispersion characteristics and geometric dimensional parameters.

This invention relates to the field of medical device sensor calibration and error compensation technology, and particularly to a method for medical device sensor calibration error compensation based on electronic measuring instruments, comprising the following steps: S1, performing multi-measurement topology switching and acquiring bridge circuit signals under zero external load conditions; S2, constructing a topology response vector and identifying electrical parameters; S3, repeatedly identifying and determining compliance ratio coefficients under different constraint conditions; S4, establishing a calibration matrix; S5, online reconstruction compensation to obtain compensated measurement values; S6, reviewing residual projection and selectively updating parameters. This invention achieves differentiated compensation for bridge circuit electrical errors and mechanical errors caused by assembly preload and changes in constraint conditions.

This invention discloses a method for analyzing multi-source monitoring data of urban air quality, relating to the field of environmental monitoring multi-source heterogeneous data fusion and digital processing technology; it addresses the problems of inconsistent physical benchmarks, distortion in temporal breakpoint repair, and delayed early warning in multi-source monitoring data. First, based on meteorological parameters, differentiated physical benchmark transformations are performed on gaseous pollutants and particulate matter to generate a normalized concentration matrix aligned with physical states. Then, a temporal topology map is constructed to map breakpoints, and gradient compensation vectors are generated using the spatiotemporal first derivative of the neighboring sensor sequences to reconstruct the continuous temporal concentration matrix. Next, the discrete deviation between standard stations and microstations is calculated, and spatial heterogeneity weighting is performed using functional zoning to generate a dynamic calibration matrix, mapping and outputting a global calibration concentration field. Finally, the step-approximation difference vector is calculated by comparing two sets of limit matrices, and extreme nodes of abrupt gradients are extracted to generate a multi-dimensional pollution risk tracing map, achieving precise location and dynamic early warning of hidden pollution sources.

The application discloses a shoe body glue spraying track extraction method based on 3D vision, comprising the following steps: using a ball center calibration method to calibrate a plurality of cameras, and obtaining a calibration matrix converted to a base coordinate system; obtaining splicing point clouds of a shoe body without a sole and a whole shoe with a sole in the base coordinate system respectively, and deleting interfering useless points; in the base coordinate system, accessing three-dimensional coordinates of corresponding points of the shoe body point cloud and the whole shoe point cloud, searching for an overlapping area to extract a glue line, and slicing the shoe body point cloud to find the lowest point as the glue line track; and adopting a curve fitting method to smooth the track as a glue spraying track of a robot glue spraying process. The method is used for automatic extraction of a glue line and glue spraying operation in shoe production, and can significantly improve production efficiency, reduce labor cost, and ensure glue spraying quality and product consistency.

The application provides a radar anti-jamming beam forming method based on an analog-digital hybrid MIMO array, comprising: constructing an array model and estimating array errors according to array element arrangement and target observation angle; generating a transmitting and receiving calibration matrix by using a least square-based transmitting-receiving separation calibration method; dividing the transmitting array into multiple sub-arrays and making the multiple sub-arrays transmit orthogonal waveforms; calibrating the phases of the array elements in the multiple sub-arrays according to the transmitting calibration matrix, and realizing beam forming by transmitting coherent signals; separating and extracting echo signals of each sub-array from the received signals by using a waveform decoding technology, and calibrating the amplitudes and phases of the echo signals by using the receiving calibration matrix; controlling the echo sidelobe level of the calibrated receiving array by using a window function, and adjusting the beam pointing direction by using the receiving end beam forming. The application can enhance the adaptability and anti-jamming capability of the radar in a complex environment, improve the radar detection probability and reduce the false alarm probability.

The application provides an Internet of Things large model pruning and compression method and system based on Attention matrix optimization, relates to the technical field of data processing, and comprises the following steps: obtaining a pre-trained Internet of Things large model, extracting parameters in an Attention matrix to obtain a parameter set to be optimized; based on the parameter set, performing importance evaluation on the parameters in the Attention matrix to generate parameter importance distribution data; taking the obtained parameter importance distribution data as a processing basis, establishing high importance calibration, medium importance calibration and low importance calibration, and constructing a ternary calibration matrix. The application effectively balances the compression ratio and inference accuracy.

The present disclosure provides a kind of based on environmental information hand-eye calibration method, device and electronic equipment, method includes: by calibration camera obtains the first environment image when mechanical arm is in first pose and the second environment image when in second pose;Determine the first pixel coordinate and the second pixel coordinate corresponding to target feature point in first environment image and second environment image;First pixel coordinate is converted into first world coordinate, and second pixel coordinate is converted into second world coordinate;Determine the imaging projection error of hand-eye calibration based on the first world coordinate and the second world coordinate of all target feature points;Imaging projection error is handled, and the hand-eye calibration matrix of hand-eye calibration is obtained, and hand-eye calibration is carried out according to hand-eye calibration matrix.The application of this method, with the target feature point in environment image instead of calibration object completes calibration, without specific calibration object, calibration process and mechanical arm operation process do not need to separate, can guarantee the consistency of calibration environment and operation environment.

This invention discloses a testing device and method for the high-frequency pulse characteristics and flow self-calibration of a hydrogen injection valve. The method includes the following steps: setting initial parameters and a target flow rate for the high-frequency pulse of the hydrogen injection valve, and constructing a nonlinear mapping model between the initial pulse duty cycle and the hydrogen output flow rate; performing a multi-gradient pulse frequency injection test; acquiring dynamic hydrogen flow rate data in real time, and simultaneously acquiring flow data during the pulse injection process; calculating the deviation between the actual flow rate and the target flow rate, and constructing a dynamic deviation evaluation model based on the stoichiometric ratio of the hydrogen fuel cell; iteratively correcting the pulse width parameter based on the deviation value, and applying a fuzzy PID control algorithm to achieve adaptive parameter optimization; outputting a calibration parameter set that meets the accuracy threshold, and generating a final parameter set containing a frequency-pulse width two-dimensional calibration matrix. This invention effectively solves the problems of flow deviation and response lag that easily occur in the hydrogen supply of the hydrogen injection valve under high-frequency pulse conditions in traditional hydrogen fuel cell systems.

The application discloses a satellite zero calibration ranging method and system based on zero value dynamic change of a switch, wherein a signal sequentially passes through a baseband board card, a switch and a self-loop zero calibration matrix and returns, a first zero calibration distance value is measured, a signal sequentially passes through the baseband board card, the switch and ground channel equipment and returns, a second zero calibration distance value is measured, and a zero value of the ground channel equipment is calculated according to the first zero calibration distance value, the second zero calibration distance value and the zero value of the self-loop zero calibration matrix. The ranging method is simple in process operation and easy to realize, the zero calibration process is increased by the self-loop zero calibration matrix, the influence of the change of the zero value of the switch on the ranging value is eliminated, and the normal work of the satellite is ensured.

The application relates to a machine case screw quick and accurate positioning method based on machine vision, and the innovation point is that the specific positioning steps comprise the following steps: S1, calibrating the position coordinates of a multi-material feeding station, and outputting eccentric distance data; S2, calculating the absolute coordinate offset between each photographing position and a reference photographing position, so that the multiple photographing positions share the same calibration matrix; S3, acquiring the grabbing coordinates and positioning coordinates of a collaborative robot, and completing multi-material transfer; S4, calibrating the position coordinates of a screw driving station, and constructing an xy special calibration matrix; S5, correcting the screw hole mechanical coordinates and completing screw driving; S6, judging missed driving and wrong driving; and S7, detecting the cover plate placement condition. The application improves the positioning and detection precision, so as to solve the technical problems that in the prior art, the multi-material feeding visual calibration is complex, the robot computing power consumption is large, the screw hole positioning deviation is prone to cause missed driving and wrong driving, and the assembly quality detection and multi-process cooperation are insufficient, and the product qualified rate is low.

An apparatus for assisting driving of a vehicle and a method thereof. An apparatus for assisting driving of a vehicle is provided, the apparatus including a camera mounted on the vehicle and configured to have a front field of view of the vehicle and acquire image data, a lidar mounted on the vehicle and configured to have a front side field of view of the vehicle and acquire lidar data, and a controller configured to process at least one of the image data or the lidar data, wherein the controller includes a calibration matrix for correcting the image data and the lidar data, and the calibration matrix is configured to: obtain a feature map of the image data and a feature map of the lidar data using machine learning, correct the calibration matrix based on a difference between the feature map of the image data and the feature map of the lidar data, and correct the image data and the lidar data using the corrected calibration matrix.

The present application relates to the technical field of robot vision servo, and provides a robot hand-eye system rapid calibration and online compensation method for dynamic grabbing, which comprises the following steps: performing system initialization and rapid hand-eye calibration with few samples, completing camera intrinsic parameter calibration, robot kinematics modeling, and time synchronization of the camera and the controller, and obtaining a hand-eye transformation matrix; uniformly modeling and pre-calibrating dynamic grabbing full-process errors, constructing four types of error observation models, calibrating full-link time delay, pre-compensating robot kinematics errors, and establishing uniform speed and uniform acceleration target motion prediction baseline models; based on online observation without fixed reference features, adaptively and jointly compensating and correcting multi-source errors in real time; based on real-time error compensation results and the target motion prediction baseline models, performing three-section visual servo dynamic grabbing closed-loop control, and driving hand-eye calibration matrix and multi-source error compensation for double closed-loop self-optimization iteration by using grabbing residuals.

The application discloses a low-illumination target detection method based on DGA-ISP, comprising the following steps: 1) constructing a normal-illumination discrete prior feature dictionary: processing and dividing a low-illumination Bayer RAW image dataset and a corresponding normal-illumination dataset, and obtaining the discrete prior feature dictionary by using a discrete feature quantization adversarial network pre-training; 2) designing a dynamic linear mapping module, extracting local and global features of the RAW image, generating an adaptive sensor calibration matrix to complete preliminary color correction; 3) designing a feature alignment and retrieval module based on reversible normalization flow, aligning the feature distribution after calibration with the normal-illumination feature, and retrieving the lossless high-quality prior feature in the feature dictionary; 4) designing a polynomial nonlinear module and a target detection network, using the retrieved prior feature to guide polynomial coefficient prediction, completing adaptive nonlinear stretching of the image and target detection, and outputting semantic category labels and spatial position bounding boxes of the target. The method combines the physical interpretability of RAW domain processing with the prior advantage of the discrete feature dictionary, can effectively suppress noise and supplement lost texture details in a low-illumination environment, and greatly improves the accuracy and robustness of target detection.

The application provides an infusion dispensing visual recognition and grabbing system and method based on improved YOLOv8, and belongs to the technical field of medical automation and machine vision. The system comprises an image acquisition module, a visual processing module, a sequence task planning module, an execution module and a control module. The visual processing module is built-in with a YOLOv8 model with triple structure optimization, adopts a phased visual positioning strategy, and the execution module comprises a mechanical arm and a surrounding independent dispensing workstation. The method comprises: hand-eye calibration and system initialization; four types of dispensing targets are recognized and positioned through improved YOLOv8; coordinates are converted through a hand-eye calibration matrix and an operation sequence is generated; the mechanical arm is driven to sequentially perform positioning grabbing, placement and drug suction injection tasks of the four types of targets in accordance with the sequence logic of positioning first and suctioning later; and the system is reset after secondary visual verification. The application realizes high-precision full-process automatic dispensing, and significantly improves the flexibility and reliability of the system.

The present application relates to the technical field of robot remote operation, and in particular to a dexterous hand remote device based on an RGBD camera and external parameter calibration and an operation method. The operation method comprises: taking a desktop as a reference object, and obtaining a transformation matrix of the RGBD camera relative to a desktop coordinate system; taking a desktop corner point as a calibration reference point, and teaching to obtain a position of an end of a mechanical arm in a mechanical arm coordinate system, and converting to the desktop coordinate system in combination with the transformation matrix to solve an external parameter calibration matrix of the mechanical arm coordinate system to the desktop coordinate system; the RGBD camera collects an image of a hand of an operator, extracts a position of a hand key point, converts to a camera coordinate system, and then converts to the desktop coordinate system through the transformation matrix to obtain a wrist pose; the wrist pose is converted into a target pose of the end of the mechanical arm, joint angles of the mechanical arm are solved, and the hand key point is remapped as joint angles of a dexterous hand. The technical scheme can improve the comfort, accuracy and safety of remote operation, and meet the demand of embodied intelligence for high-quality remote operation data collection.

The application discloses an IMU gyroscope noise reduction method and system based on deep learning, and belongs to the technical field of inertial navigation and deep learning. The method comprises the following steps: collecting IMU original measurement data and obtaining a real attitude increment; inputting historical measurement data in the IMU original measurement data into an extended convolutional neural network, jointly extracting time sequence correlation features of a gyroscope and an accelerometer by the extended convolutional neural network, and outputting a gyroscope time-varying correction value; establishing a gyroscope correction model; performing linear transformation on the original gyroscope measurement value by using a learnable gyroscope internal parameter calibration matrix of the model, superimposing the time-varying correction value, and obtaining corrected gyroscope data; and performing open-loop integration on the corrected gyroscope data, and calculating an estimated attitude increment. The application can achieve sub-degree attitude precision under the condition of pure inertia without visual dependence.