36 results about "Gauss newton method" patented technology

The invention discloses an unmanned aerial vehicle cluster collaborative target hunting method based on brain-like calculation, and belongs to the technical field of autonomous navigation robot and multi-robot coordination control, and the method comprises the steps: obtaining a multi-view image under a camera view, and obtaining the local pose and covariance of each unmanned aerial vehicle; obtaining a global consistent relative pose of the unmanned aerial vehicle cluster according to the local pose and the covariance of each unmanned aerial vehicle; performing state estimation on the target by adopting an unscented Kalman filtering algorithm to obtain target state information under a unified coordinate system; using a Bezier curve generation algorithm to obtain a target motion trail in a section of historical state; the method comprises the following steps: expanding an unmanned aerial vehicle cluster through a centroid extension method to obtain a surrounding queue of target surrounding, solving the minimum surrounding cost by adopting a Gaussian Newton method, and generating a surrounding position of each unmanned aerial vehicle; the motion trajectory of the unmanned aerial vehicle is generated by adopting mixed A * search, and a final surrounding trajectory sequence is generated, so that the accuracy of motion state estimation is remarkably improved.

The invention belongs to the technical field of geophysics. According to the truncation model space three-dimensional magnetotelluric inversion method and system, a three-dimensional inversion objective function is constructed according to an inversion initial model, the inversion objective function is optimized by adopting a Gaussian-Newton method, and a Gaussian-Newton increment equation is obtained, a sensitivity matrix in the Gaussian-Newton increment equation is a sparse matrix which is obtained after truncation is carried out by adopting a distance truncation threshold value and a sensitivity amplitude truncation threshold value, the Gaussian-Newton increment equation is converted into an unconstrained least square form, and an inversion updating direction is determined according to the least square form; and performing iterative optimization according to the initial inversion model, the inversion updating direction and the model updating step length to obtain an updated inversion model. According to the invention, units with small contribution to inversion updating are effectively cut, and the model space corresponding to each piece of data is reduced.

The invention discloses a vehicle spatial position uncertainty modeling method based on a factor graph, and relates to the technical field of automatic driving. Establishing a factor graph model, and constructing state variable nodes and factor nodes; an objective function of the factor graph optimization problem is converted into a nonlinear least square problem, and optimal state estimation is obtained through iterative solution by adopting a Gauss-Newton method; on the basis of an objective function under a Gaussian Newton method framework, a posterior covariance matrix is obtained through calculation of a Hesse matrix and inversion; performing incremental optimization on the factor graph, and updating a global Jacobian matrix through matrix decomposition and Givens transformation; and carrying out confidence ellipse geometric quantization, and representing the positioning uncertainty in a geometric form. Based on a factor graph and a confidence ellipse theory, through multi-sensor space-time constraint fusion and geometric interpretable representation, a vehicle positioning uncertainty modeling framework is constructed, and the safety and robustness of an automatic driving system downstream decision planning algorithm are improved.

The invention discloses a self-adaptive weight-adjusting high-precision joint inversion method based on electromagnetic gradient data driving, and relates to the technical field of electromagnetic signal inversion, and the method comprises the steps: calculating the space gradient data of original electromagnetic field observation data through employing a Gaussian weighted neighborhood method; combining the conventional data residual item, the data residual item and the regularization item to construct a joint objective function; and embedding the gradient sensitive scale matrix into a data residual term of the joint objective function, establishing an inversion equation, carrying out iterative calculation on the inversion equation, updating model parameters, and obtaining a three-dimensional inversion result reflecting resistivity spatial distribution. According to the method, the classical Gaussian-Newton method is expanded, the electromagnetic field data and the residual term of the spatial gradient are utilized to perform inversion optimization, and the gradient-sensitive scale matrix is introduced into residual weighting, so that only model parameters related to the abnormal region can be updated in each inversion iteration, and the accuracy of inversion optimization is improved. And mapping to a three-dimensional finite element domain for forward modeling calculation.

The invention provides a regional navigation positioning method, system and device based on sequential information, and a medium, which can effectively improve the positioning precision and reduce the calculation complexity when processing the sequential information, and have better precision and robustness. Under a satellite navigation denial condition, aiming at distance measurement information at different moments obtained by land-based radio area navigation, a factor graph model is established to represent an information transmission process between different moment states of a positioning target. Compared with a traditional Kalman filtering framework, the factor graph algorithm is better in positioning precision, higher in flexibility, lower in calculation complexity and better in robustness when sequential information is processed. According to the method, state estimation is converted into a nonlinear least square optimization problem, a Gaussian Newton method is adopted for iterative solution, and compared with extended Kalman filtering, the matrix operand is reduced by more than 40%.

The fusion adaptive dynamic prior information magnetotelluric and magnetic method joint inversion method belongs to the field of geophysical data processing and inversion technology; by constructing a joint inversion objective function including data fitting term, model constraint term and physical coupling term, and using Gauss-Newton method for solving, the core innovation lies in introducing adaptive dynamic prior information extraction technology; the method can automatically generate and optimize prior constraints from data itself and intermediate results, without relying on external preset information, thereby significantly improving the accuracy and reliability of the boundary description and physical property recovery of underground abnormal bodies (such as resistivity, magnetic susceptibility anomaly); solve the problem that the traditional physical property coupling joint inversion technology has strong dependence on prior information and weak application generalization ability in information missing area.

The invention discloses a vehicle-mounted positioning method and system based on a tetrahedron UWB anchor point cluster. The method comprises the following steps: obtaining TDOA data and AOA data obtained by four UWB anchor points in a vehicle according to UWB signals sent by UWB equipment; constructing a hyperbolic equation according to the position information of the four UWB anchor points and the four groups of TDOA data; solving the hyperbolic equation to obtain the initial position of the UWB equipment; forming a non-coplanar tetrahedron according to the four UWB anchor points, and constructing a constraint condition of volume conservation of the tetrahedron; performing iterative optimization on the initial position based on a Gaussian Newton method, and introducing the constraint condition to constrain an iterative optimization process to obtain an optimized position; and performing weighted correction on the optimized position according to the four groups of AOA data to obtain a final position. Based on the data processing flow, error suppression is carried out by fusing TDOA fast convergence, AOA direction correction and volume constraint, so that four-anchor-point high-precision positioning is realized.

The invention relates to the technical field of automatic driving, and discloses a multi-sensor fusion SLAM method and system considering curvature constraints. According to the method, kinematics constraints of an Ackerman steering vehicle are fully considered on the basis of an LIO inertial laser radar tight coupling system, and the method comprises the steps that firstly, the rear axle wheel speed and the front wheel rotating angle are utilized and combined with a vehicle two-degree-of-freedom model to construct a vehicle odometer constraint factor; secondly, establishing a relationship among a yaw angle, a wheel speed, an X-axis displacement and a Y-axis displacement, and adding curvature constraint factors under the same turning direction; and finally, constructing SLAM rear-end optimization based on a factor graph method, adding an IMU pre-integration factor, a laser radar odometer factor, a vehicle odometer constraint factor and a curvature constraint factor, and solving by adopting a Gaussian Newton method. A test result in a real scene shows that compared with an LIO-SAM algorithm and an FAST-LIO2 algorithm, the precision of the algorithm provided by the invention is obviously improved.

This invention discloses an electromagnetic eddy current defect imaging method and system based on parameter level sets. The method includes optimizing the parameters of an array of eddy current sensors; acquiring data from the metal component under test to obtain eddy current response signals under different excitation-reception conditions; reconstructing the three-dimensional defects based on a parameter level set reconstruction algorithm; setting the location of discrete point sources, the conductivity values ​​of the metal and defects, and the initial coefficient parameters of the basis functions; inputting the eddy current response signals into the level set reconstruction algorithm model to obtain the defect reconstruction objective function based on the parameter level set; calculating the Jacobian matrix and iteratively solving the objective function using the Gauss-Newton method, performing iterative updates, and determining whether the defect reconstruction has converged based on whether the deviation between the reconstruction results before and after the iteration is less than a threshold; and obtaining the final defect imaging result. This invention is easy to implement and does not require a mechanical scanning device; it can acquire information on internal surface defects of metal components through computational imaging.

The fusion adaptive dynamic prior information magnetotelluric and magnetic method joint inversion method belongs to the field of geophysical data processing and inversion technology; by constructing a joint inversion objective function including data fitting term, model constraint term and physical property coupling term, and using Gauss-Newton method for solving, the core innovation lies in introducing adaptive dynamic prior information extraction technology; the method can automatically generate and optimize prior constraints from data itself and intermediate results, without relying on external preset information, thereby significantly improving the accuracy and reliability of the boundary description and physical property recovery of underground abnormal bodies (such as resistivity, magnetic susceptibility anomaly); solve the problem that the traditional physical property coupling joint inversion technology has strong dependence on prior information and weak generalization ability in information missing area.

The invention provides a multi-wave joint inversion method and device, electronic equipment and a storage medium. The multi-wave joint inversion method comprises the steps of determining a longitudinal wave-longitudinal wave angle gather and a longitudinal wave-transverse wave angle gather in a longitudinal wave-longitudinal wave angle gather time domain in seismic data; determining formation parameters of a wellhead of the receiving well, and determining an initial inversion model according to the formation parameters of the wellhead; according to the initial inversion model, determining a longitudinal wave-longitudinal wave synthesis record and a longitudinal wave-transverse wave synthesis record by using an accurate vertical transverse isotropic medium reflection coefficient equation; determining a real stratum model, determining relationships between the longitudinal wave-longitudinal wave synthesis record and the longitudinal wave-longitudinal wave angle gather and between the longitudinal wave-transverse wave synthesis record and the longitudinal wave-transverse wave angle gather by using a Bayesian framework, and determining an optimal model of the real stratum model by using a maximum posterior probability; and linearizing the accurate vertical and transverse isotropic medium reflection coefficient equation by using a Gaussian Newton method, determining the update quantity of the initial inversion model, and updating the initial inversion model by using the update quantity.

The invention relates to an efficient forward modeling transient electromagnetic three-dimensional inversion method and device and computer equipment. The method comprises the following steps: randomly generating a plurality of transient electromagnetic three-dimensional geoelectric models, carrying out emission source decomposition on each three-dimensional geoelectric model, constructing an independent sub-grid for each independent source and each receiving point according to the skin depth of each time channel, converting a large-scale global problem into a large number of small-scale sub-problems, and generating a plurality of transient electromagnetic three-dimensional geoelectric models; and the sub-problems are efficiently distributed to a large number of computing cores for parallel processing. Based on the efficient parallel forward modeling capability, it is possible to construct a large-scale and high-density three-dimensional training data set, and a foundation is laid for subsequent application of a deep learning model. And then, a high-precision prior model of deep learning prediction is used as a hot start initial model, and is introduced into a traditional inversion process based on a Gaussian Newton method, so that rapid, stable and high-resolution transient electromagnetic three-dimensional inversion is realized. According to the invention, the precision and efficiency of underground structure imaging are significantly improved.

The invention discloses a multi-source information fusion integrated navigation method and system independent of satellite navigation, and belongs to the field of navigation and positioning of autonomous mobile platforms. Establishing a vision-laser-inertial odometer and polarized light sensor measurement model, establishing an inertial navigation system motion model, initializing the integrated navigation system, and determining the attitude of the integrated navigation system according to a carrier state node position constraint, a course constraint and an adjacent state node pose transformation constraint at the current moment; establishing an integrated navigation system state vector and a measurement error function to obtain a to-be-optimized factor graph, and optimizing the to-be-optimized factor graph by using a Gaussian-Newton method to obtain optimal estimated values of the carrier position, speed and attitude under a world coordinate system with the minimum error function. According to the method, multi-source information is fused, information contributions are balanced through optimization of the factor graph, the robustness and reliability of the system in a satellite navigation signal rejection environment are remarkably improved, and the method has good practical engineering significance.

The application discloses a kind of unmanned aerial vehicle cluster cooperative target encirclement methods based on brain-like computing, belong to autonomous navigation robot and multi-robot coordination control technical field, method includes: obtaining multi-view image under camera visual angle, obtain the local pose and covariance of each unmanned aerial vehicle;According to the local pose and covariance of each unmanned aerial vehicle, obtain the global consistent relative pose of unmanned aerial vehicle cluster;Using unscented Kalman filtering algorithm, the state of target is estimated, and the state information of target in unified coordinate system is obtained;Using Bezier curve generation algorithm, the target motion trajectory under a period of history state is obtained;The method of centroid extension is used to expand the unmanned aerial vehicle cluster, and the encirclement queue of target encirclement is obtained, the least encirclement cost is solved using Gauss Newton method, and the encirclement position of each unmanned aerial vehicle is generated;The motion trajectory of unmanned aerial vehicle is generated using hybrid A* search, and the final encirclement trajectory sequence is generated, which significantly improves the accuracy of motion state estimation.

The present application relates to a kind of unmanned aerial vehicle low-altitude visual navigation method based on motion blur auxiliary, comprising: acquisition monocular camera image and IMU data, quantification the image blur degree of the monocular camera image, to extract the blur parameter of dominant motion, for calculating global measurement blur data;The IMU data is pre-integrated, obtains the relative motion amount between adjacent frames, and based on system prior state estimation, the image plane motion field of current time is predicted, for simulating global theoretical blur data;The global measurement blur data and the global theoretical blur data are compared, and after being combined with adaptive weight, as motion blur consistency error, construct nonlinear least squares optimization objective function;According to the nonlinear least squares optimization objective function, Gauss-Newton method iteration is solved, generates the state vector of minimum total error, for calculating health score, to implement three-level mode switching.

The application provides a multi-level fusion optimization method and system for oblique photogrammetry space three processing, and relates to the technical field of photogrammetry, wherein the method comprises: acquiring multi-view image data and constructing an image connection graph, using weighted spectral clustering combined with multi-constraint spectral clustering for dynamic blocking to form a subnetwork with internal connection and overlapping relationship. Based on the image quantity, point cloud scale and matching complexity evaluation and calculation load, the subnetwork is distributed to distributed computing nodes, and the subnetwork parameters are optimized in parallel. The target overlapping area is determined through multi-level subnetwork fusion, the public points and camera pose are extracted to construct a pose graph model, and the Gauss-Newton method is used to solve the global consistent transformation parameters. Under the constraint of ground control points, overall optimization is carried out through weighted least squares adjustment, a global model is finally constructed, light beam adjustment is performed, and the space three processing result is generated. The application improves the automation degree and three-dimensional reconstruction precision of oblique photogrammetry data processing.

A method and device for reconstructing moving target trajectories for a wide-angle staring SAR based on the Gauss-Newton method include performing sub-aperture division on the original radar echo signal; performing two-dimensional compression on the original echo signal of each channel in the range and azimuth dimensions to obtain the range-Doppler domain echo of each channel; and preprocessing the range-Doppler domain echo in the range-Doppler domain; extracting the phase history and envelope of the moving target based on the preprocessed range-Doppler domain echo to estimate the radial velocity of the moving target; using a maximum likelihood algorithm to estimate each order phase parameter in the extracted phase history; obtaining the motion parameters of the moving target based on the relationship between each order phase parameter and the motion parameters; obtaining a rough motion trajectory reconstruction result based on the motion parameters of the moving target; segmenting the rough motion trajectory of the moving target to obtain the precise reconstructed position of the moving target in each segment; and splicing the reconstructed positions to obtain a complete motion trajectory reconstruction result.

The invention provides a multi-level fusion optimization method and system for oblique photography aerial triangulation processing, and relates to the technical field of photogrammetry, and the method comprises the steps: obtaining multi-view image data, constructing an image connection graph, carrying out the dynamic partitioning through the combination of weighted spectral clustering and multi-constraint spectral clustering, and forming a subnet with an internal connection and overlapping relation. And based on the image quantity, the point cloud scale and the matching complexity, evaluating the calculation load, distributing the subnets to distributed calculation nodes, and optimizing subnet parameters in parallel. A target overlapping area is determined through multi-level subnet fusion, a common point and a camera pose are extracted to construct a pose graph model, and a Gaussian-Newton method is adopted to solve global consistency transformation parameters. Under the constraint of a ground control point, overall optimization is carried out through weighted least square adjustment, finally, a global model is constructed, lightweight beam adjustment is executed, and an aerial triangulation processing result is generated. According to the invention, the automation degree of oblique photography data processing and the three-dimensional reconstruction precision are improved.

The present application relates to a vibration model constraint-free terahertz SAR image self-focusing method, and belongs to the technical field of terahertz SAR. The present application aims to solve the problem that the image focusing is insufficient due to the limitation of a specific vibration model in the terahertz SAR image focusing process, and the nonlinear equation solving process in the self-focusing process is relatively complex. The process is as follows: obtaining a terahertz SAR echo signal; the terahertz SAR echo signal contains phase errors; obtaining the image entropy of a terahertz SAR image; taking the first-order derivative of the image entropy S of the terahertz SAR image with respect to the phase error and making the first-order derivative of the image entropy S with respect to the phase error zero to obtain a nonlinear equation to be solved; solving the nonlinear equation based on the Gauss-Newton method to obtain a phase error estimation value that is not constrained by a specific model; based on the phase error estimation value, constructing a compensation function to obtain a final clear SAR imaging result.

The invention discloses a total station free station setting three-dimensional adjustment method taking reference point errors into account, which comprises the following steps: S1, constructing a station center coordinate system O-XYZ, and calculating station center coordinates of free station setting measurement points such as a reference point, a broken part point and the like; s2, constructing a coordinate transformation model considering the reference point priori coordinate and the station center coordinate error, then performing iterative solution through a Gaussian-Newton method, and obtaining a transformation parameter estimation reference point station center coordinate correction V station between the reference point priori coordinate and the station center coordinate; and S3, calculating three-dimensional space coordinates of the broken part points. According to the method, a space rectangular coordinate conversion adjustment model considering reference point prior coordinates and station center coordinate errors is constructed, two sets of reference point three-dimensional space coordinate conversion parameters conforming to the actual free station setting measurement are obtained through iterative solution by adopting a Gaussian-Newton method, and the three-dimensional space coordinates of broken part points are fitted and estimated; the method solves the problem that the three-dimensional adjustment of free station setting of the total station cannot consider the influence of the datum point error at present.

This invention discloses a method for identifying robot kinematic parameter errors based on the accuracy allocation of end-effector pose calibration. The method includes: acquiring the joint angles of n robot positioning points and their corresponding end-effector measurement poses; determining the weight range of the accuracy allocation for the robot end-effector pose calibration; initializing relevant parameters to obtain an initial weight of pose calibration accuracy; determining the fitness function of the optimization algorithm; fusing the pose calibration accuracy weight with the Gauss-Newton method to identify robot kinematic parameter errors; updating the fitness function value and the pose calibration accuracy weight until a termination condition is met to obtain the robot kinematic parameter errors. This invention can determine the pose calibration accuracy allocation weight without multiple measurements of the robot end-effector pose, significantly saving time and costs; compared with traditional pose calibration methods, it can significantly improve the robot's pose accuracy.

The invention belongs to the technical field of numerical control machine tool geometric error precision measurement and identification, and provides a five-axis gantry machine tool position-related geometric error modeling and identification method and system, and the method comprises the steps: introducing and building a transmission relation between a geometric error and a space error, and constructing a kinematic model; distance information of the tail end position of the machine tool is measured, and actual position coordinates of the measuring points are solved; using a polynomial function to represent the position-related geometric error, and defining a measurement reference at an original point of a coordinate system; and identifying the polynomial function, and identifying and decoupling the error according to the error basic characteristics. According to the method, high identification precision is guaranteed while only the distance information multilateral measurement method is used, all geometric errors of the translation axis and the rotation axis of the five-axis machine tool are identified, error decoupling is achieved in the error identification process, the problem that a Jacobian matrix is singular in the Gauss-Newton method is solved, and the error identification precision and efficiency are improved.

The embodiment of the invention provides a map construction method and system based on continuous time and curved surface features, and the method comprises the steps: removing motion distortion through point cloud preprocessing, querying a neighbor point set from a local point cloud map, and fitting a quadratic polynomial curved surface; a continuous time pose concept is fused, poses at the beginning and ending moments of a frame of point cloud are used as variables to construct a point-to-curved surface residual error, an optimization problem is designed, and a Gaussian Newton method is adopted to iteratively solve an optimal pose; meanwhile, by updating the local map and cutting voxels exceeding a set range, the high efficiency of the map structure is maintained; the method makes full use of curved surface features such as common trees and telegraph poles in an automatic driving scene, further gets rid of dependence on a plane structured environment and an inertial measurement unit by means of continuous-time high-frequency estimation, improves the adaptability of the laser odometer to a complex environment, effectively solves the problem of insufficient constraint caused by insufficient plane features, and improves the accuracy of the laser odometer. And the mapping precision and stability are obviously improved.

The present disclosure provides a DC / IP data efficient parallel inversion method and system, relating to the technical field of geophysical exploration, comprising: obtaining DC / IP actual observation data of a target area, and obtaining synthetic observation data by adding noise; constructing an initial underground model; based on the synthetic observation data, iteratively updating the initial underground model by the Gauss-Newton method, linearizing the optimization problem of DC / IP inversion to obtain a normal equation; according to the synthetic observation data and the current model parameters, implicitly calculating the Jacobian matrix by the pseudo-forward technology, solving the linear equation set of pseudo-forward by the direct solver, and performing parallel calculation by the hybrid MPI+OpenMP technology; according to the Jacobian matrix and the normal equation, solving the model parameter increment, and updating the underground model by using the model parameter increment; the present disclosure realizes accurate inversion of DC / IP data, and is convenient for popularization and use.

The invention provides a delay estimation method of a high-precision dynamic control platform, and belongs to the technical field of vehicle control. According to the method, a modulation function is introduced to organize a control signal transmission model into a linear regression form, and a recursive least square method is adopted to carry out iterative solution, so that an estimated value of the response delay of the actuator is obtained; introducing a modulation function to organize the control signal transmission model into a function related to the communication delay, and solving the function by adopting a Gaussian Newton method to obtain an estimated value of the communication delay; and constructing a joint optimization model, performing optimization by taking joint delay minimization of the response delay and the communication delay of the actuator as a target, and outputting optimal estimation values of the response delay and the communication delay of the actuator. According to the method, unknown derivative values of the input signal and the output signal can be converted into known derivative values of the self-defined modulation function, accurate online estimation of the system delay under noise interference is ensured, and the noise robustness during estimation is improved.

The invention provides a wireless access point positioning method based on received signal strength. The wireless access point positioning method comprises the steps that S1, RSS original data of a target wireless access point are collected through receivers, and corresponding RSS observation values are obtained through processing according to the RSS original data; s2, constructing a to-be-estimated parameter vector containing a target position coordinate, a path loss index and reference power of the target wireless access point; s3, based on a path loss model, establishing a nonlinear model function between the RSS observed value and the to-be-estimated parameter vector; and S4, constructing a least square problem according to the nonlinear model function, and carrying out iterative solution by adopting a Gaussian-Newton method to obtain a least square estimated value of the to-be-estimated parameter vector as a target position coordinate of the target wireless access point. The method has the advantages that the method can automatically adapt to different indoor positioning scenes, tedious parameter calibration in advance is not needed, and high positioning precision and calculation efficiency can be kept.

The application discloses an adaptive weight-adjusting high-precision joint inversion method based on electromagnetic gradient data driving and relates to the technical field of electromagnetic signal inversion. The application comprises the following steps: calculating the spatial gradient data of the original electromagnetic field observation data by using a Gaussian weighted neighborhood method; combining a conventional data residual term, a data residual term and a regularization term to construct a joint objective function; embedding a gradient sensitive scale matrix into the data residual term of the joint objective function to establish an inversion equation; iteratively calculating the inversion equation and updating model parameters to obtain a three-dimensional inversion result reflecting the spatial distribution of resistivity. The application extends the classic Gauss-Newton method, optimizes the inversion by using the residual terms of the electromagnetic field data and the spatial gradient, introduces the scale matrix sensitive to the gradient in the residual weighting, so that only the model parameters related to the abnormal area can be updated in each inversion iteration, and the model parameters are mapped to the three-dimensional finite element domain for forward calculation.

The invention discloses a multi-layer casing conductivity and magnetic conductivity inversion method, which belongs to the field of electromagnetic nondestructive testing, and comprises the following steps: determining a transient response expression of a multi-layer ferromagnetic casing under pulsed eddy current excitation by adopting a pulsed eddy current testing rapid analysis model; converting an electromagnetic parameter measurement problem of the multilayer ferromagnetic sleeve into an optimization problem, wherein the optimization problem aims at minimizing an error between an actually measured transient response and a theoretical transient response calculated by a transient response expression; solving the optimization problem by adopting a Gaussian-Newton method to obtain coupling parameters related to the conductivity, the relative permeability and the wall thickness of the sleeve; based on the known wall thickness of each layer of the multi-layer ferromagnetic sleeve, the coupling parameters and the wall thickness of the corresponding layer are calculated, and the conductivity and the relative permeability of each layer of sleeve are obtained; according to the sequence from the bottom of the well to the ground, operation is carried out on different levels in the multi-layer ferromagnetic casing in sequence, and inversion of the conductivity and the relative permeability of the casing of all the levels is completed.

The application provides a kind of rotary body part installation error identification method based on elliptical error model, and the analysis result can judge rotary body part geometric eccentricity, phase, end face swing angle, direction simultaneously, to provide conditions for next step accurate detection or processing.The detection principle is to measure the complete contour line of a circle in radial direction of rotary body part by displacement sensor (such as contact scanning probe, non-contact optical probe, etc.) to obtain original relative data set, to obtain contour change data set by wavelet decomposition, to obtain relative data set for optimization by further processing.Due to the influence of rotary body part swing, the measurement section which is theoretically circular will become elliptical, and it is easy to know that the short axis length b of elliptical section is consistent with the actual radius of the measured rotary body part.Using improved Gauss-Newton method to optimize the objective function combined with the data set for optimization, and adjusting the threshold value and increment coefficient of each parameter of increment matrix according to the different iteration convergence speed of optimization parameters.

The present invention discloses a two-dimensional laser radar positioning method based on intersection point constraints, which belongs to the technical field of robot positioning. The method includes acquiring point cloud data of an indoor environment based on a two-dimensional laser radar; extracting straight line segments from the point cloud data using a seed region growing method; classifying the straight line segments based on an adaptive axis selection strategy based on the main extension direction of the line segments, and establishing a straight line matching relationship between adjacent frames based on the intersection information between the straight line segments and the coordinate axes; based on the straight line matching relationship between adjacent frames, minimizing the distance residual from the point to the line according to the Gauss-Newton equation to obtain the robot's posture information. Based on the matching results, the present invention uses the Gauss-Newton method to minimize the distance residual from the point to the line to obtain the robot's posture. The entire method has low computational time and can still maintain high efficiency under high point cloud density. It is suitable for low-power processors and meets the requirements of indoor robots for lightweight and real-time performance.