456 results about "Quaternion" patented technology

The invention relates to the technical field of visual identification, and discloses a belt tearing detection method, device and equipment based on visual identification, and the method comprises the steps: carrying out the dual-channel image collection and preprocessing of the surface of a conveying belt, and obtaining a multi-channel preprocessing image; extracting a thermal difference feature and a texture structure feature of the multi-channel preprocessed image through a double-flow feature network, and inputting the thermal difference feature and the texture structure feature into a quaternion material deformation analysis model for deformation gradient tensor and invariant parameter calculation to obtain a tear feature description vector; performing hierarchical progressive identification and three-dimensional reconstruction analysis to obtain target tearing feature data; and risk assessment is carried out based on the target tear feature data to obtain a tear grade classification result, the interference of ambient temperature drift and non-uniform illumination is effectively eliminated, the calculation efficiency and accuracy of tear detection are improved, and false alarms and missing alarms are reduced.

The invention discloses a neural network inertial attitude estimation method fusing physical information constraints. The method comprises the following steps: acquiring IMU data and attitude quaternion values at corresponding moments; performing windowing processing on the IMU data, and screening an attitude quaternion value as an attitude quaternion pseudo-true value based on physical consistency; training the constructed neural network prediction model; optimizing the neural network prediction model through a model loss function, wherein the model loss function comprises a physical constraint loss function and a supervision loss function; and during target carrier attitude estimation, inputting IMU data of a corresponding window into the trained neural network prediction model, and calculating to obtain a target carrier attitude quaternion estimated value of the corresponding window. According to the method, the generalization ability of the neural network is enhanced through physical information constraint, the precision and robustness of inertial attitude estimation are improved, and the method is particularly suitable for inertial navigation application in a high-dynamic environment.

The invention belongs to the field of robot control, and particularly relates to a robot self-adaptive impeller welding control method and system fused with multi-modal data, and the method comprises the steps: obtaining an impeller workpiece image and three-dimensional point cloud data through an industrial vision subsystem, and extracting a three-dimensional welding seam position point set representing a welding seam track; generating a candidate welding path set based on welding seam geometric feature analysis; in combination with welding seam morphology and process requirements, optimal welding process parameters are predicted through a regression model, and a welding gun attitude angle is calculated through quaternion conversion according to a curved surface normal vector; in the welding process, the relative position deviation of a robot and a workpiece and the parameter deviation of welding current / voltage and a planned value are monitored in real time, when the deviation exceeds a preset tolerance interval, a PID control algorithm is adopted to dynamically adjust robot motion parameters or welding process parameters, and closed-loop control over the welding process is achieved; according to the method, precise planning of the welding path and self-adaptive regulation and control of the process are achieved, and the welding quality and the forming consistency are remarkably improved.

The invention provides a robot teleoperation method and a robot. The robot teleoperation method comprises the steps that an acceleration command, an angular acceleration command and an expected attitude quaternion of an end effector are determined based on the current linear acceleration, the current angular acceleration and the current attitude quaternion of a control end; the current position and the current speed fed back by the end effector are obtained, a torque control signal for controlling the position of the end effector is determined based on the acceleration command, the current position and the current speed, and end acceleration feedforward is obtained; determining a torque control signal for controlling the attitude of the end effector based on the angular acceleration command and the expected attitude quaternion to obtain attitude feedforward; and a joint torque instruction of a mechanical arm joint corresponding to the end effector is determined based on the end acceleration feedforward and the posture feedforward, and the joint torque instruction is sent to a corresponding joint driver so that the end effector can synchronously act along with the control end. According to the invention, the problem of attitude lag of the end effector can be avoided, and the tracking error of the end effector is reduced.

The invention discloses a pose estimation method and device based on point pair feature matching, a medium and a product. The method comprises the following steps: performing coarse registration on a fusion scene point cloud and each template point cloud, and calculating point pair features; according to the corresponding hash key values, retrieving from each hash table to establish registration point pairs; calculating a translation vector and a rotation quaternion matched with each registration point pair according to the reference point coordinate system and the template coordinate system, and performing clustering processing on each registration point pair to obtain each initial corresponding set; screening out a target pose candidate transformation matrix and a matched target template point cloud from the pose candidate transformation matrixes matched with the initial corresponding sets respectively; a target optimization function between the fusion scene point cloud and the target template point cloud is constructed, iterative optimization is performed on parameters in the target pose candidate transformation matrix, and the target pose of the fusion scene point cloud is obtained at the end of iteration, so that the precision, real-time performance and reliability of six-dimensional pose estimation are improved.

The invention discloses a fusion filtering attitude estimation method based on EKF and Mahony, and the method comprises the steps: firstly obtaining the original data information of an IMU sensor at the current moment, carrying out the normalization of the attitude information of an accelerometer and a magnetometer, carrying out the error calculation, and compensating the error to the angular velocity of a gyroscope through a PI controller, the method comprises the following steps: performing Mahony filtering correction on an angular velocity and a corrected attitude quaternion to obtain an angular velocity subjected to Mahony filtering correction and a corrected attitude quaternion, constructing a state equation of an EKF by using the corrected attitude quaternion and a gyroscope zero offset value, and constructing an observation equation by using data of an accelerometer and a magnetometer so as to obtain an optimally estimated attitude quaternion and a gyroscope zero offset value; and finally, compensating the optimally estimated zero offset value of the gyroscope to the angular velocity correction process in the Mahony filtering process.

The invention relates to an all-humanoid robot motion redirection method and device based on motion capture equipment, and the method comprises the steps: converting a human body posture quaternion collected by the motion capture equipment into a rotation matrix, and achieving the precise mapping from a Cartesian space to a robot joint space in combination with an Euler angle decomposition strategy; a quaternion-disassembled joint angle is used as an initial solution, various physical constraints are introduced in real time according to the actual supporting state of the robot, and the lower limb joint angle is corrected online by constructing and solving an inverse kinematics problem with various constraints, so that the robot reproduces the action of an operator, and the joint angle of the lower limb can be corrected online. And the requirements of joint limiting, self-collision avoidance, dynamic balance and the like are strictly met, so that the accuracy of motion simulation and the stability of the system are effectively unified.

The invention discloses an LED spherical display screen-oriented geographic data dynamic adaptive display method, which comprises the following steps of: constructing a non-uniform grid mapping model based on LED spherical screen physical parameters (diameter, support structure cavity size and pixel row / column spacing); the method comprises the following steps: accurately mapping geographic data (converted into longitude and latitude coordinates in advance) from a longitude and latitude coordinate system to a spherical screen pixel space, and carrying out resampling adaptation on the data based on the resolution of a display; meanwhile, a quaternion rotation transformation algorithm and a dynamic mask technology are adopted, the shielding area of the supporting structure of the LED spherical screen is calculated in real time, invisible data are automatically cut in the three-dimensional rotation process, and the problems of coordinate mapping distortion, rotation tearing and shielding area adaptation delay existing in a traditional LED spherical display system are solved. According to the method, the sub-pixel-level mapping precision of the geographic data on the LED spherical screen and the self-adaptive shielding compensation response of real-time rotation are realized.

The invention discloses a ship dynamic collision avoidance decision-making method based on an improved DWA algorithm. The ship dynamic collision avoidance decision-making method comprises the steps of constructing sea chart environment data containing obstacle density through a grid method; establishing a dynamic elliptical ship domain based on a quaternion ship domain (QSD) model, and calculating a distance collision risk degree (NCRI) between ships based on the dynamic elliptical ship domain; improving a DWA algorithm evaluation function, and adding a path yaw evaluation function and a collision avoidance rule evaluation function on the basis of optimizing a speed and azimuth angle evaluation function; fusing the multiple evaluation functions by adopting a weight distribution method; and finally, generating a local collision avoidance optimization route based on environment data, NCRI and an improved DWA algorithm. According to the method, safety boundary dynamic adjustment, rule compliance constraint and ship control characteristic cooperation are realized, and the robustness and practicability of collision avoidance decision making under complex sea conditions are remarkably improved.

The invention discloses a precise and synchronous collaborative data signal analysis method based on dual-channel optical fiber sensing, and relates to the field of collaborative data signal analysis, polarization compensation and nonlinear noise decoupling analysis are carried out on time domain optical signals collected by independent A and B sensing channels to obtain polarization dimension-increasing coherent collaborative signals, and the collaborative data signal analysis is carried out on the time domain optical signals. Performing quaternion mapping and transformation compensation analysis to obtain a pure polarization state of strain immunization, performing fractional differential, HOSVD decomposition and time varying Jacobian matrix propagation disturbance based on the pure polarization state of strain immunization to obtain a non-equilibrium entanglement feature vector, combining quantum Bayesian update and a KLD weight model, generating a particle set through state sampling, and obtaining a non-equilibrium entanglement feature vector. And performing directional transfer entropy calculation on the particle set, constructing a weighted Dirichlet clustering model and directional causal energy flow analysis, comparing dual-channel energy values to obtain a channel fault judgment result, and performing channel fault judgment timely and accurately.

The invention relates to the technical field of attitude determination of a photoelectric pod of an unmanned aerial vehicle, in particular to an MEMS-IMU dual-mode correction attitude determination method for the photoelectric pod of the unmanned aerial vehicle. Comprising the following steps: establishing a damping system second-order transfer function model, and outputting an attenuation coefficient matrix; based on MEMS-IMU three-axis motion parameters, distinguishing a large maneuver turning state and a linear flight state; respectively compensating a roll angle, a pitch angle and a course angle of the MEMS-IMU by using the main inertial navigation according to different states; converting the corrected attitude angle into a quaternion, and optimizing and outputting a fused quaternion through a gradient descent method; taking the fused quaternion as an input, constructing a seven-dimensional state space model, and outputting an error compensation amount through Kalman filtering; dynamically adjusting the main inertial navigation weight according to the accelerated speed root mean square; and outputting a final attitude angle in combination with the error compensation amount and the weight, and verifying the final attitude angle through GNSS (Global Navigation Satellite System) position inversion. The method has the advantages that cross interference is avoided; therefore, the method is suitable for the photoelectric pod scene of the unmanned aerial vehicle.

The invention provides a train attitude precision keeping method and system based on inertial quaternion modeling, and belongs to the technical field of train operation positioning. Through a quaternion modeling train attitude feedback compensation model, auxiliary calibration of a gyroscope is realized by using an accelerometer, and calculation of an inertial attitude is realized; converting the acquired wheel speed sensor information into three-dimensional speed under a navigation coordinate system through attitude information resolved by a quaternion modeling train attitude feedback compensation model, and further integrating to obtain a position; and fusing the speed and position information of the wheel speed sensor after attitude conversion with the speed and position information obtained by inertial navigation dead reckoning to realize error correction of the inertial navigation system. According to the method, the problem that the train state sensing precision is degraded due to the fact that a satellite navigation / inertial navigation system positioning model is difficult to overcome inertial device error accumulation when a satellite fails is solved, the train attitude precision is kept, and then error divergence of a combined positioning system during satellite signal denial is effectively restrained.

The invention relates to the technical field of inertial sensing attitude monitoring, and particularly discloses a body motion attitude monitoring method and system based on an inertial sensing technology, and the method comprises the steps: collecting the original data of a body motion attitude, outputting an attitude quaternion after fusion and an angular velocity vector after calibration, carrying out the dynamic attitude feature extraction, and carrying out the calibration. And outputting a body motion posture feature vector, carrying out stability margin modeling, obtaining a stability margin scalar, determining a tumble probability update value, carrying out tumble evolution prediction, combining the tumble probability update value, outputting a tumble probability and a probability change trend, obtaining a tumble early warning level, analyzing adaptive calibration parameters and carrying out feedback. The problems that in the prior art, multi-sensor data cannot be effectively integrated, so that the information utilization efficiency is low, and the potential of multi-modal data fusion is not fully exerted are solved, and the monitoring accuracy can be continuously optimized.

The invention discloses a learning-based composite layered anti-interference control system and method suitable for a large-windward-area unmanned aerial vehicle, is used for realizing robust wind resistance control of the large-windward-area unmanned aerial vehicle, and aims at the flight characteristics of large windward area and sensitivity to external wind field change in a vertical take-off and landing stage. A wind speed estimation method without an additional sensor is provided, modeling of external disturbing force is realized in combination with a Gaussian process regression algorithm, and compensation of the disturbing force is realized by using quaternion-based model prediction control, so that the influence of an external change wind field on the dynamics of the large-windward-area unmanned aerial vehicle is reduced, and the trajectory tracking precision is remarkably improved. A backstepping controller based on SO (3) is designed in an attitude control loop, a nonlinear disturbance observer is integrated, and active compensation of external disturbance torque and robust tracking control of attitude are realized. Based on the technical characteristics, a complete dynamic control link capable of estimating and feeding back compensation disturbance in real time is further constructed.

Provided is a generalization generation method of industrial robot pose trajectories supporting changeable operation path points, which relates to the field of robot trajectory planning. The method includes: acquiring a plurality of groups of robot end pose trajectories; aligning time steps of the pose trajectories via a multi-dimensional dynamic time warping algorithm; constructing a Gaussian mixture model of robot pose trajectories in combination with a variational Bayesian method and an attitude quaternion tangent space mapping method; calculating reference pose trajectory distribution via a Gaussian mixture regression method; performing kernelized representation on pose trajectory distribution, and solving an optimal hyperparameter of a kernel function by minimizing a root mean square error of a reproduced reference trajectory; and generating the robot end pose trajectories adapted to the operation path points by updating the reference pose trajectory distribution.

The invention relates to the technical field of attitude control, in particular to a moving object attitude control method based on an improved LKF. Predicting prior attitude estimation at the next moment; calculating a process error covariance matrix; calculating a measurement attitude; calculating a quaternion measurement error covariance matrix; constructing feature vectors of acceleration interference and geomagnetic interference amplitude levels, and calculating an interference amplitude level value by using a Gaussian function model; updating the process error covariance matrix and the measurement error covariance matrix by using the interference amplitude level value; calculating a priori attitude estimation error covariance matrix by using the updated process error covariance matrix; calculating a Kalman gain matrix by using the priori attitude estimation error covariance matrix and the updated measurement error covariance matrix; calculating attitude posteriori estimation at the next moment; and performing attitude estimation at the next moment. According to the method, the problems of insufficient estimation precision and poor interference suppression capability of the attitude observation error covariance matrix of the existing LKF are solved.

The invention discloses a target tracking method and device based on a rolling and pitching type holder, and the method comprises the steps: building a polynomial response model through adaptive color calibration, and achieving the dynamic color correction; an improved YOLOv8 model is combined with a bidirectional feature pyramid network to carry out multi-scale target detection, and template matching is assisted to improve a low-confidence-coefficient scene recognition rate; predicting the position and the speed of the target by using Kalman filtering to cope with a shielding condition; and high-precision smooth rotation of the holder is realized through quaternion attitude solution in combination with PID (Proportion Integration Differentiation) and FOC (Fiber Operating Control) double-loop cooperative control. The device adopts a rolling and pitching type holder structure and comprises a main controller, an angle sensor, a visual sensor and a magnetic encoder, the mechanical structure is simplified, the weight is reduced, and higher response speed and higher control precision are achieved. The target tracking accuracy, stability and anti-interference capability of the intelligent mobile platform in a complex environment are remarkably improved, and the method is suitable for various dynamic scenes such as aerial photography and industrial inspection.

The invention discloses a radiation correction method and system for an airborne hyperspectral imager. The radiation correction method and system are used for improving the radiation correction precision in complex weather. The method comprises the following steps: calculating a theoretical sun position based on unmanned aerial vehicle initial coordinates, ephemeris data and atmospheric parameters; performing course and differential compensation on a solar azimuth angle by combining IMU course and wind speed and direction data, performing refraction correction on an elevation angle, and outputting a high-precision sun position parameter; hyperspectral images and irradiance data are synchronously acquired, quaternion interpolation is combined with IMU data to realize space-time alignment, and attitude disturbance is eliminated; a hemispherical space irradiance model is constructed based on alignment data, cloud layer modes (clear sky, thin cloud and thick cloud) are divided according to atmospheric optical thickness variance, and Lambert body proportion correction, a discrete longitudinal standard method or a deep convolutional spectral network are respectively adopted for radiation correction. The method remarkably improves the correction precision and generalization ability, supports large-area image splicing, and is suitable for quantitative application of geological exploration, environment monitoring and the like.

The invention relates to the technical field of performance testing, and discloses a performance testing method, device and equipment for a graphics card, and the method comprises the steps: carrying out the quaternion stage decoupling of a rendering pipeline of the graphics card, and obtaining a vertex shader decoupling unit, a geometric shader decoupling unit, a rasterization unit decoupling unit and a pixel shader decoupling unit; stage isolation performance tests are respectively carried out to obtain first performance test data of the vertex shader, second performance test data of the geometric shader, third performance test data of the rasterization unit and fourth performance test data of the pixel shader; the first performance test data, the second performance test data, the third performance test data and the fourth performance test data are comprehensively analyzed to obtain performance bottleneck positioning results of all stages of the rendering pipeline, the coupling states of all the stages of the rendering pipeline can be effectively separated, and accurate adjustment and control over the running states of all the stages are achieved.

The invention discloses a point cloud reconstruction method and system based on perception and dynamic initialization, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: extracting point cloud data with position and color information from a multi-view image, converting the point cloud data into a CUDA tensor, and carrying out the processing, thereby achieving the dynamic initialization of 3D Gaussian distribution and parameters; a spherical harmonic function is adopted to process color features, an adjacent point average distance is adopted to determine initial Gaussian scaling, a quaternion is adopted to initialize a rotation angle, and an inverse sigmoid is adopted to set an initial opacity value, so that the initialization of a 3D model is optimized; a rendered image is generated by using a 3D-to-2D sputtering technology, and 3D Gaussian distribution parameters are optimized through pixel-level L1 loss, local structure-level SSIM loss and perceptual loss LPIPS. According to the method, 3D Gaussian distribution is dynamically initialized, and perception loss optimization is introduced, so that the problem of time overhead of a 3DGS method in an iteration process is solved, and the problem that the 3DGS method is easy to have a fuzzy detail or distortion phenomenon in scenes such as complex textures and high-frequency regions is solved.

The invention discloses a manned aircraft / unmanned aerial vehicle pursuit deep learning control method simulating a pigeon positive and negative feedback mechanism. The method comprises the following steps: step 1, giving mathematical models of a manned aircraft and an unmanned aerial vehicle and performing environment initialization; step 2, building a quaternion PID controller of the unmanned aerial vehicle; 3, a manned aerial vehicle / unmanned aerial vehicle hierarchical decision-making architecture is established; step 4, mathematical modeling of a pigeon positive and negative feedback mechanism; 5, designing a manned aerial vehicle / unmanned aerial vehicle cluster reward function simulating positive and negative feedback of the pigeon flock; 6, planning and designing an escape unmanned aerial vehicle path; step 7, predicting an escape unmanned aerial vehicle path; and 8, generating a manned aerial vehicle / unmanned aerial vehicle cluster pursuit learning strategy based on the actor-commentator network. According to the invention, an efficient, flexible and extensible bionic intelligent pursuit control framework is constructed, and the pursuit task execution efficiency and cooperative combat capability of the manned aerial vehicle / unmanned aerial vehicle heterogeneous cluster in a complex dynamic environment are improved.

The invention relates to the field of array signal processing, and particularly discloses a DOA joint estimation method based on a quaternion polarization sensitive array. Electromagnetic wave dual polarization components are captured through the orthogonal dipole and the loop antenna, and a quaternion observation matrix is constructed; calculating a quaternion covariance matrix by adopting a sliding window mechanism, and separating a signal / noise subspace by adopting quaternion singular value decomposition; and constructing a spatial spectrum function in combination with a quaternion steering vector, and realizing joint estimation of an azimuth angle and a pitch angle through two-dimensional search. According to the method, the unified characterization capability of quaternions on polarization-airspace information is fully utilized, and the DOA estimation precision and the anti-interference performance of the multi-polarization signal are remarkably improved.

The invention relates to the technical field of visual positioning, and provides a 66kV transformer substation drainage wire clamp visual guidance dismounting method and device and a medium, through identifying two to-be-dismounted assemblies longitudinally arranged in a drainage wire clamp image and assembly types, the to-be-dismounted assemblies can be conveniently and subsequently processed in a targeted manner according to the assembly types; plane fitting is carried out through the point cloud information, the data dimension is effectively reduced, the data processing efficiency is improved, and accurate target positioning can be assisted. A pitch angle, a yaw angle, a yaw angle and a central point between two to-be-assembled and disassembled components are calculated according to plane parameters, and calculation is further simplified in combination with conversion from an Euler rotation angle to quaternion, so that the positioning efficiency is improved; the rotation matrix is corrected according to the coordinate system of the mechanical arm, the output matrix is adjusted according to the center point and the corrected rotation matrix, the matrix finally output to the mechanical arm is more accurate, and therefore the mechanical arm can be assisted in efficiently and accurately conducting pose adjustment, and live-line disassembly and assembly are conducted on the drainage wire clamp.

The invention discloses a positioning and attitude determination method and device integrating Beidou navigation and inertial navigation, and relates to the technical field of attitude measurement, the method comprises the following steps: correcting a gyroscope error by using an accelerometer error and a magnetometer error based on proportional integral operation to obtain a gyroscope output after error correction; using the gyroscope output after error correction to update the quaternion of the moving object, using the quaternion to calculate the attitude, and obtaining the attitude angle of the moving object under the carrier coordinate system calculated through inertial navigation; moving object positioning and attitude determination calculation is carried out by using the time difference of the satellite carrier phase double differences between the double BDS receivers and the different groups of satellites, and the attitude angle of the moving object under a carrier coordinate system calculated through Beidou satellite navigation is obtained; and performing Kalman filtering fusion on the attitude angle calculated by inertial navigation and the attitude angle calculated by Beidou satellite navigation to obtain a final attitude angle. The method can adapt to a dynamic environment, and the attitude measurement precision is improved.

The invention relates to the field of industrial robot automatic grabbing, and discloses a swivel nut robot grabbing system and method.The system comprises an image acquisition module, a background segmentation module, a feature fusion module, a pose regression module, a position calculation and judgment module, a motion execution module, a synchronous triggering module and a safety control module; processing to generate a point cloud; extracting CNN texture and PointNet + + geometric features of the point cloud, and splicing and outputting fusion features; the three-dimensional coordinates and the quaternion rotation amount of the key points are regressed and converted into six-dimensional poses, accessibility is judged, and effective poses are output; and the six-axis mechanical arm controller receives the signals and then executes grabbing. The camera is triggered to collect when the thread sleeve enters the visual field, the pose confidence coefficient is monitored in real time, and the servo power supply is cut off when the pose confidence coefficient is low. The position and posture of the thread sleeve can be accurately obtained so as to control the six-axis mechanical arm to grab, through multi-module cooperation, an advanced algorithm and real-time monitoring, the grabbing accuracy and safety are improved, and the operation risk is reduced.

The present invention discloses a method for identifying fancy rope skipping. The rope skipping device includes a 3-axis acceleration sensor, a 3-axis gyroscope, and a classifier. The 3-axis acceleration sensor is used to collect acceleration data of the 3 axes, and the 3-axis gyroscope is used to collect angular velocity data of the 3 axes. The acceleration and angular velocity data of the rope skipping are collected at the same sampling rate. The acquired angular velocity and acceleration data are fused into a quaternion. Each axis of the quaternion is subjected to a simplified bandpass filtering process. A feature window T1 within a preset time period is determined and searched. Feature data within the feature window T1 is extracted. The data in the feature window is input into a classifier, and the classifier identifies and determines whether it is a crossover action and performs a validity judgment. The present invention utilizes the quaternion data formed by the fusion of acceleration and angular velocity data to identify crossover actions in fancy rope skipping. This method does not require excessive resource consumption and can complete offline recognition at the rope skipping end.

The invention discloses a parameter self-adaptive control method for a low-orbit satellite with a solar panel driving device. Belongs to the technical field of aerospace, and particularly relates to the technical field of low-orbit satellite parameter control. The satellite attitude control method solves the technical problems of overcoming the influence of the rotational inertia of the satellite and compensating the influence of the SADA vibration disturbance torque and the atmospheric resistance torque on the satellite attitude control precision. The method comprises the following steps: constructing a satellite attitude kinematics and dynamics equation in a quaternion form, and constructing a satellite attitude kinematics and dynamics equation for designing an attitude controller on the basis of the satellite attitude kinematics and dynamics equation; defining an error attitude kinematics and dynamics equation of the satellite through the attitude control error and the angle control error; carrying out segmentation conversion on a to-be-estimated parameter and a known state variable, and expressing the negative influence of a rotational inertia error on the attitude by matrix operation; designing the control torque of the attitude controller by adopting a mode of combining nonlinear feedback control with self-adaptive control; and updating the self-adaptive estimated value in the control parameters of the attitude controller by adopting a parameter self-adaptive law.

The invention discloses a magnetic positioning resolving method and system, and relates to the technical field of magnetic positioning and sensor resolving, the method successfully separates an attitude error from magnetic field drift by explicitly introducing a rotation quaternion and bias modeling, has good physical interpretability and environmental adaptability, and can be applied to the field of magnetic positioning and sensor resolving. The precision, the robustness and the generalization ability of the magnetic positioning system in a complex environment can be remarkably improved, and the method is suitable for various application scenes such as robot navigation, industrial detection and medical equipment.

The invention relates to the technical field of magneto-rheological polishing calibration, in particular to a TCP calibration method for robot magneto-rheological polishing based on three-coordinate measurement, which comprises the following steps of: measuring different point positions on a polishing wheel in a magneto-rheological polishing module by using a three-coordinate measuring instrument to obtain a sphere center position of a spherical surface on which the polishing wheel is positioned; measuring a normal vector of any curved surface on the polishing wheel by using a three-coordinate measuring instrument, and determining a rotation matrix of the TCP based on the normal vector; the rotation matrix is converted into a quaternion, the quaternion is input into the robot, and TCP attitude correction is completed; according to the sphere center position and the spherical radius, the working coordinates of the TCP and the offset of the TCP relative to the center of the robot flange plate are determined; and the working coordinates and the offset are input into the robot, and TCP position correction is completed. According to the invention, dual calibration of the TCP position and attitude is realized, the whole process does not depend on artificial experience, no complex algorithm is needed, the operation is simple and convenient, the calibration is efficient, and the precision is reliable.

The invention discloses an intelligent processing method and system for multi-source data fusion of surveying and mapping equipment, and belongs to the technical field of surveying and mapping data processing.According to the method and system, an initial LiDAR point cloud is combined with position data and attitude data for joint calculation to obtain an accurate LiDAR point cloud, the accuracy of point cloud data is optimized, and therefore the accuracy of final registration is improved, and the registration accuracy is improved. And matching the point cloud multi-scale feature point set with the image multi-scale feature point set to obtain a point cloud homonymy point set and an image homonymy point set, analyzing a rotation matrix and a translation vector through a quaternion registration algorithm, and completing registration of the target panoramic image and the precise LiDAR point cloud according to the rotation matrix and the translation vector. And high-precision space registration is realized through a quaternion registration algorithm, the registration accuracy is further improved, and the surveying and mapping precision is improved.