737 results about "Rotation matrix" patented technology

The invention belongs to the technical field of numerical control machine tool machining, and particularly discloses a numerical control machine tool machining reference point correction method and system, a terminal and a medium. Based on the three-dimensional coordinate information, the actual position of a preset reference point on the machine tool is extracted and compared with a preset theoretical reference point, and a deviation value is calculated; executing coordinate deviation aggregation analysis according to the deviation value, and determining coordinate system correction parameters including a translation vector and a rotation matrix; the corrected parameters are issued to a numerical control machine tool control system, and a current workpiece coordinate system is updated; and after the coordinate system is updated, the machining task continues to be executed, the machining state is monitored in real time, and if it is detected that the deviation exceeds a threshold value, the measurement and correction process is automatically triggered again. Automatic correction of the reference point and real-time compensation of the workpiece coordinate system can be achieved, the machining precision is improved, manual intervention is reduced, and the method is suitable for high-precision numerical control machining scenes.

The invention relates to a high-precision hand-eye calibration method for a robot system. The method comprises the following steps: planning a plurality of random robot poses; in the process of operating the pose of the robot, the coordinate value of a target ball point is measured through a laser tracker, an end effector tool coordinate system is constructed and converted into a robot base coordinate system, and an actual measurement rotation matrix and a displacement vector are obtained through calculation. In combination with the theoretical rotation matrix and the displacement vector, pose compensation is achieved through a rotation operator and a translation operator which are finely adjusted, and the target pose and displacement of the robot are adjusted; and after compensation, repeatedly measuring the actual measurement pose and calculating the residual error, and taking the residual error as a compensation value of next iteration until the residual error meets a preset precision threshold value or reaches the maximum number of iterations. And finally, recording the corresponding relation between the pose of the calibration plate in the 3D camera coordinate system and the theoretical pose of the robot to complete the robot hand-eye calibration process. According to the method, the calibration precision is improved, and the high-precision machining and assembling requirements are met.

The invention discloses a point cloud registration method, system and device and a medium, and relates to the technical field of three-dimensional reconstruction of scenes, and the method comprises the steps: obtaining a source point cloud and a target point cloud; semantic segmentation is carried out according to spatial structure characteristics of the point clouds, and plane points and non-plane points of the source point clouds and the target point clouds are obtained respectively; two-way distance search constraints are applied to planar points and non-planar points in the source point cloud and the target point cloud respectively, and a corresponding relation of two-way matching constraints is established; point-to-surface registration and point-to-point registration based on the maximum correlation entropy criterion are carried out on the plane points and the non-plane points after bidirectional constraint, and rotation matrixes and translation vectors of the plane points and the non-plane points are respectively obtained through multiple iterations; weighting the rotation matrix and the translation vector according to an adaptive weight, and applying the weighted rotation matrix and translation vector to the source point cloud to obtain the source point cloud after pose transformation; according to the method, the nearest neighbor corresponding relation from the source point cloud to the target point cloud and from the target point cloud to the source point cloud is considered, and the matching accuracy of the corresponding points is improved.

The invention discloses a heading equipment cutting head position calibration method based on image acquisition, which comprises the following steps: S1, acquiring image data and preprocessing the image data; s2, extracting cutting head feature points, and constructing a two-dimensional feature point set; s3, calculating a three-dimensional coordinate by using perspective transformation, and generating a point cloud set; s4, performing rigid registration on the point cloud set and a standard structure model of the tunneling equipment, and iteratively solving a rotation matrix and a translation vector to obtain pose parameters; s5, simulating change tracks of the feature points in continuous frames, constructing a time consistency constraint, and optimizing pose parameters; s6, the optimized three-dimensional coordinates are converted to a reference coordinate system, space alignment is completed, and a position calibration result is generated; and S7, outputting a calibration result for control instruction generation and path planning. According to the invention, high-precision and non-contact three-dimensional position calibration of the cutting head of the tunneling equipment is realized, and operation automation and control precision are improved.

The invention discloses an unmanned ship displacement attitude high-precision fusion method based on adaptive Kalman filtering, and belongs to the technical field of ship sensing positioning, and the method comprises the steps: building a discrete time state space model of a navigation data state through employing a discrete integration mode based on the navigation data state; obtaining a system state equation by means of a rotation matrix and a median theorem; obtaining an observation transfer matrix through coordinate system information, a geodesic line length between two points and an azimuth angle, and obtaining a state observation equation by combining noise data; based on the system state equation and the state observation equation, using an adaptive Kalman filtering algorithm to obtain displacement attitude information of the unmanned ship; detecting the prediction data of the unmanned ship by using a sliding window method; and when the predicted data is detected to be abnormal, substituting the predicted data into Kalman filtering for correction. According to the invention, through state modeling, fusion algorithm, anomaly detection and anomaly correction, the precision, reliability and robustness of obtaining the displacement attitude information of the unmanned ship are improved.

The invention discloses a monocular vision pose measurement method and system based on a circular feature target spot, and the method comprises the following steps: camera calibration, image collection, image preprocessing, and feature point detection and matching: employing Hough circle detection to extract the center two-dimensional pixel coordinate and radius of the circular feature target spot, obtaining a uniquely determined relationship between the feature target spots according to the circle center coordinates and the radius information; performing pose measurement, and obtaining a rotation matrix R and a translation vector T which are converted from a target coordinate system in each collected pose image to a camera coordinate system according to a PnP algorithm; and solving the relative pose. According to the method, a specially designed cooperative target containing five circular characteristic target spots is used as a target object, and a rapid detection algorithm realized according to target characteristics is combined, so that circle center matching sorting is realized, and the precision is improved. The method has the advantages of being simple in structure, simple in camera calibration, large in measurement view field, high in real-time performance and the like, and meanwhile the defects that the view field is small and stereo matching is difficult in stereo vision are overcome.

A point cloud decoding method related to the field of coding technologies and includes reconstructing a point cloud comprising one or more patches. The one or more patches comprise a current patch. The reconstructing process may include transforming coordinates (x2, y2) of a second point of the current patch in a second coordinate system to coordinates (x1, y1) of a first point of the current patch in a first coordinate system, where the coordinates (x1, y1) of the first point of the current patch in the first coordinate system may be obtained based on the coordinates (x2, y2) of the second point of the current patch in the second coordinate system and a transform matrix.

The invention relates to the technical field of machine vision, in particular to a precision part size automatic detection method and system based on machine vision, precision part images are collected through a high-precision industrial camera, part positioning is carried out, sub-pixel-level topological feature mapping is carried out on interested area images, and precision part size automatic detection is carried out. Comprising the steps of gray histogram equalization, gray morphological processing, edge detection and edge chain code tracking, construction of an edge point topological feature space, execution of sub-pixel subdivision, obtaining of an edge line through contour analysis of a feature distance and a feature angle, and double-constraint geometric reconstruction based on the edge line. The characteristic distance and the characteristic angle are used for rotation matrix conversion and geometric dimension calculation, a relation model of the geometric dimension and the actual dimension of the part is established, precise part dimension measurement is achieved through dynamic error analysis and compensation, the measurement precision is remarkably improved, the risk caused by unreliability of a single characteristic is effectively reduced, and the measurement accuracy is improved. And the measurement stability is improved.

The invention relates to a course axis drift suppression method and device, a holder and a medium, and the method comprises the steps: receiving original data of a current course, the original data comprising an absolute angle outputted by a course encoder, and an acceleration and an angular velocity outputted by an IMU module; the original data are converted into a unified coordinate system through a rotation matrix, and mechanical installation deviation is compensated; performing data fusion on the compensated original data through a preset fusion algorithm; and outputting the fused absolute angle to a servo mechanism of the holder, and by adopting the method, through real-time sensor data alignment, a dynamic compensation algorithm and a fusion algorithm, a course drift suppression mechanism which is resistant to dynamic interference and low in cost is constructed, and static drift lt can be realized; the dynamic tracking error is 1t, 0.01 degree / h and 1t; and the technical index is 0.05 degree.

The invention belongs to the technical field of computer vision, deep learning and time-space prediction, and discloses an end-to-end time-space prediction method based on improved three-dimensional rotation position coding, which is suitable for various time-space sequence prediction scenes such as weather, traffic flow and the like. According to the invention, through four key improvements, a position coding mechanism is optimized; three-dimensional coding proportions of time, height and width are dynamically adjusted so as to adapt to different scenes; fusing the absolute time and the relative space position, and strengthening local space-time correlation modeling; the position information directly guides attention calculation, and the fusion with an Attention module is deepened; and a rotation matrix cache mechanism is introduced to reduce redundant calculation. Meanwhile, the model is matched with a Patch embedding layer, an adaptive Transform encoder and an MLP de-wharf, a complete link of'feature embedding-position encoding-space-time fusion-prediction output 'is constructed, and the precision, generalization and reasoning efficiency of space-time prediction are effectively improved.

Embodiments of the present application relate to the field of computer vision. Disclosed are a target six-degree-of-freedom pose estimation method and apparatus based on binocular vision and particle swarm optimization. The method comprises: obtaining a binocular image of a target, wherein the binocular image at least comprises a rotation matrix, a displacement vector and geometric features of the target; decomposing the rotation matrix into a rotation vector and a rotation angle by means of a rotation matrix decomposition method, and performing six-degree-of-freedom uniform sampling on the rotation vector and the rotation angle to obtain a data point set; establishing a mathematical relationship between the rotation matrix and the displacement vector on the basis of the geometric property of the geometric features and the data point set; and performing target pose estimation on the binocular image of the target by designing an objective function and an application optimization algorithm to obtain a pose estimation result. The present application solves the problems in the related art of low accuracy, poor universality and poor robustness.

The invention belongs to the technical field of space remote sensing surveying and mapping, and particularly relates to a monitoring system and method for a front and rear view visual axis included angle and absolute direction of a surveying and mapping camera. The method comprises the following steps: S1, through side sway of a satellite platform, taking a time for sky imaging calibration as a reference time; s2, calculating a rotation matrix from a front and rear view ground camera measurement coordinate system to a star camera coordinate system at a reference moment; s3, calculating respective thermal deformation matrixes of the star camera coordinate system, the front-view camera measurement coordinate system and the rear-view camera measurement coordinate system; s4, obtaining a real-time rotation matrix from the measurement coordinate system of the front-view camera to the coordinate system of the star camera and a real-time rotation matrix from the measurement coordinate system of the rear-view camera to the coordinate system of the star camera at the measurement moment; and S5, carrying out arc cosine calculation on the two absolute pointing vectors to obtain a visual axis included angle between the front-view camera and the rear-view camera at the measurement moment. According to the invention, while the geometric accuracy of the surveying and mapping camera is ensured, the real-time performance of measurement and the comprehensiveness of subsystem monitoring are realized at the same time.

The invention discloses a remote sensing image cloud removal method and system based on geographic coordinate embedding, and the system employs a geographic coordinate system embedding module to encode pixel longitude and latitude information into high-dimensional features, and achieves the joint modeling of an absolute position and a relative space relation through spherical coordinate mapping and a rotation matrix. And in combination with a multi-head self-attention mechanism and residual connection, the position sensitivity of the feature space is enhanced. Meanwhile, by using a multi-level feature extraction network and a cloud distortion compensation module, through a residual block and convolution operation, cloud interference features are extracted, a position sensing correction vector is generated, nonlinear offset of a cloud coverage area is corrected, and earth surface textures are recovered. According to the method, the adaptability of a cross-regional scene is remarkably improved, texture distortion and information loss of a large-scale cloud region are effectively reduced, the method has the advantages of high precision and high robustness in the fields of remote sensing image preprocessing, agricultural monitoring, land cover change analysis and the like, and a new scheme is provided for high-quality application of optical remote sensing data.

The invention discloses a three-dimensional point cloud registration method and system based on multi-feature dynamic weighting. The method comprises the following steps: firstly, acquiring a source point cloud and a target point cloud to be registered and performing down-sampling; secondly, extracting geometric features of the source point cloud and the target point cloud, including curvatures and normal angles of all points; extracting a target point from the target point cloud, performing point pair matching by using a multi-constraint weighted scoring mechanism and a dynamic weight optimization strategy, and taking a point pair with a minimum dynamic weighted difference function value as a matching point pair; screening the matching point pairs according to the normal angle difference threshold value and the curvature difference threshold value, if the normal angle difference score is smaller than or equal to the normal angle difference threshold value and the curvature difference score is smaller than or equal to the curvature difference threshold value, reserving the matching point pairs, and otherwise, rejecting the matching point pairs; and finally, solving a rotation matrix and a translation vector according to the reserved matching point pairs to realize point cloud registration. The problems that a traditional ICP algorithm is sensitive to the initial attitude of the point cloud, the anti-noise capability is weak, and feature utilization is insufficient are solved.

The invention discloses a bridge tower section attitude intelligent prediction method and system based on multi-point cooperative localization, and the method comprises the steps: S100, selecting a plurality of observation points on two adjacent side surfaces of an installed section, obtaining the space coordinates of the observation points through a laser reflector, and carrying out the cooperative measurement through a plurality of total stations; s200, performing attitude change prediction on the to-be-installed segment based on the rigid body transformation mathematical model, calculating the optimal attitude of the to-be-installed segment by using a rotation matrix and a translation vector, optimizing attitude estimation by excessive-point redundant information, and controlling an accumulative error; s300, performing BIM modeling on the bridge tower structure, performing high-precision three-dimensional rendering, converting complex monitoring data into a visual graphical interface, and displaying a segment state in real time; s400, according to data comparison, analysis is carried out in combination with errors, and if the errors with design data are large, corresponding adjustment is carried out; and S500, the steps are repeated till all the bridge tower sections are accurately positioned and installed. And the erection precision and the construction efficiency of the bridge tower sections are improved.

The invention provides a method for acquiring the position and attitude of a model through a single camera, and belongs to the technical field of wind tunnel measurement. The method comprises the following specific steps: S1, installing a plurality of to-be-tested coding points on a model; s2, the model is adjusted to an initial attitude, so that the incidence angle, the roll angle and the sideslip angle of the model are all zero; s3, acquiring three-dimensional coordinates of a to-be-measured coding point in the wind tunnel coordinate system, shooting an image of the model by using the main camera, and acquiring two-dimensional pixel coordinates of the to-be-measured coding point in the image; s4, calculating a rotation matrix and a translation vector from a two-dimensional pixel coordinate to a three-dimensional coordinate; s5, moving the model, repeating the step S3, and calculating a rotation matrix and a translation vector from a two-dimensional pixel coordinate to a three-dimensional coordinate; s6, on the basis of the data in the step S4 and the step S5, calculating and acquiring pose information of the model and outputting the pose information; and S7, repeating the steps S5 and S6, and obtaining and outputting the pose information of the model in real time. The data obtained by the method provided by the invention is high in precision and more accurate.

The invention belongs to the field of helicopter rotor blade motion parameter measurement, and relates to a blade deformation measurement method based on rotation-following vision. The method comprises the steps that LED mark points are installed on the upper surfaces of the paddles; a follow-up rotating binocular stereoscopic vision system used for shooting the upper surfaces of the blades obliquely and downwards is mounted above the propeller hub; images of the LED mark points are collected through a rotation-following binocular stereoscopic vision system according to the test state; identifying pixel coordinates of the LED mark points in the image; according to an internal reference matrix, a rotation matrix and a translation matrix, obtained through pre-calibration, of the following-rotation binocular stereoscopic vision system, the pixel coordinates of the LED mark points are converted into three-dimensional coordinates under a coordinate system of the following-rotation binocular stereoscopic vision system; and converting the three-dimensional coordinates of the LED mark points in the coordinate system of the follow-up rotation binocular stereoscopic vision system into the three-dimensional coordinates of the LED mark points in the coordinate system of the propeller hub, thereby calculating the deformation of the propeller blade.

The invention relates to the technical field of image registration, and discloses a point cloud and image registration method and system. The method comprises the steps that a calibration plate and a calibration plate coordinate system are constructed, the position of the calibration plate is changed, three-dimensional point clouds of a target object when the calibration plate is located at different positions are obtained through a laser radar, and two-dimensional images of the target object when the calibration plate is located at different positions are obtained through a camera; internal parameters and external parameters of the camera are determined through the calibration tool and the two-dimensional image; according to the three-dimensional point cloud, the two-dimensional image and the calibration plate, calculating a rotation matrix and a translation vector between a laser radar coordinate system and a camera coordinate system; projecting the three-dimensional point cloud to a camera coordinate system according to the rotation matrix and the translation vector to obtain a two-dimensional coordinate corresponding to the three-dimensional point cloud; and determining two-dimensional image pixels corresponding to the two-dimensional coordinates according to the two-dimensional coordinates, the internal reference and the external reference, and assigning the pixels of the two-dimensional image to the three-dimensional point cloud to obtain registration data. According to the invention, the accuracy of registration can be improved.

The invention discloses a 360 vehicle-mounted around-view camera calibration method and device based on ground texture features, and the method comprises the steps: enabling a camera with calibrated internal parameters to be installed and fixed on a vehicle, measuring the installation position of the camera, controlling the vehicle to go straight at a low speed on a flat and wide ground with certain textures, detecting and matching ground feature point sets of adjacent frames, and carrying out the calibration of a 360-degree vehicle-mounted around-view camera. Calculating the position and attitude change of the vehicle by using the internal reference and the feature points; further calculating 3d coordinates of the ground feature points, and fitting to obtain a ground normal vector; calculating a rotation matrix of a camera coordinate system and a world coordinate system, and calculating multiple groups of vectors by using multiple frames of data to optimize the rotation matrix; and obtaining a top view of each camera by using the rotation matrix and the camera position initial value, collecting feature points of adjacent overlapping areas, and performing position optimization on all cameras of the surround view system. According to the method, calibration auxiliary tools are not needed, a site does not need to be printed in advance, calibration can be achieved only through a flat ground with certain textures, and the possibility of errors caused by personnel operation and measurement is reduced.

The invention discloses an unmanned aerial vehicle pose determination method and device, electronic equipment and a storage medium, and belongs to the field of unmanned aerial vehicle pose determination, and the method comprises the steps: synchronously obtaining a visible light image, a depth image and inertial data of an unmanned aerial vehicle, and extracting a vanishing point set from the visible light image, extracting a plane normal vector set and a feature point set from the depth image; based on geometric constraint conditions that the vanishing point direction is aligned with a preset orthogonal direction and the plane normal vector is perpendicular to the orthogonal direction, constructing a rotation matrix solving model, and optimizing a rotation matrix by taking minimization of a three-dimensional map point projection error as a target; and meanwhile, combining feature point depth information and an inertia pre-integration error, constructing a translation vector solution model for joint optimization, and finally fusing a rotation matrix and a translation vector to output the pose of the unmanned aerial vehicle. Therefore, by implementing the method, the problem of pose drift caused by lack of geometric constraints in the prior art can be solved.

The invention relates to an X-ray fluoroscopic image generation method and device and an X-ray fluoroscopic examination system. The generation method comprises the steps that a target sight vector for a target organ is determined according to observation requirements and the target position where the target organ of a living body needing to be detected is located; determining a primary rotation angle and a secondary rotation angle of an imaging arm in the X-ray fluoroscopy equipment based on the target sight line vector, and determining a primary rotation matrix and a secondary rotation matrix; determining a target projection matrix conforming to a target projection geometrical relationship based on the primary rotation matrix and the secondary rotation matrix; and based on the target projection matrix and pre-acquired three-dimensional computed tomography (CT) data of the target organ, performing volume rendering to obtain a two-dimensional target X-ray perspective image under the target observation view angle. According to the generation method and device of the X-ray perspective image and the X-ray perspective inspection system, the X-ray perspective image under the expected observation view angle can be freely generated based on the three-dimensional CT data obtained in advance.

The invention discloses a robot hand-eye calibration method, control equipment and a robot system, and relates to the technical field of robots. The robot hand-eye calibration method comprises the following steps: uniformly correcting a plurality of pixel coordinates obtained by dynamic shooting to a reference pixel coordinate system which is consistent with a camera coordinate system at a reference position in direction; and meanwhile, the world coordinates of the end effector corresponding to each sampling position are localized relative to the world coordinates of the end effector at the reference position, so that interference caused by global coordinate offset and camera orientation difference is eliminated, and the application range of the hand-eye calibration method is expanded. Besides, a singular value decomposition algorithm is adopted to solve a mapping relation between pixel coordinates and local world coordinates under a reference pixel coordinate system, so that a solved rotation matrix can meet orthogonality constraints, the hand-eye transformation process strictly conforms to a robot motion model with translation and rotation motion, and the calibration precision is improved. The calibration process can be automatically calculated and completed, and the calibration efficiency is high.

The invention discloses a binocular structured light three-dimensional reconstruction and dynamic rotation matrix-based large rotation equipment rotor multistage stacking concentricity accurate measurement method, and belongs to the technical field of non-contact optical measurement, three-dimensional reconstruction and assembly precision measurement. The method comprises the following steps: 1, binocular structured light three-dimensional reconstruction; step 2, dynamic rotation matrix correction: correcting the space attitude of the surface of the spigot, and converting all measured point clouds to a unified coordinate system for concentricity calculation; 3, multi-stage stacking eccentricity error evaluation under the polar coordinate system is carried out, the concentricity is rapidly calculated on the basis of a three-dimensional reconstruction result, and visual distribution of eccentricity errors is formed for subsequent online compensation; and 4, constructing a feed-forward + feedback compensation closed-loop system based on the measurement and error transfer model in the step 3, and correcting the eccentric error in real time in the assembly process. The defects that existing contact measurement is low in efficiency, contact damage exists, and multi-stage assembly accumulative errors cannot be evaluated and compensated in real time are overcome.

The invention provides a processing method and system for scoliosis detection based on point cloud features. According to the system, a depth camera is used for monitoring the dynamic state of a subject through a point cloud data acquisition module, and back three-dimensional point cloud information is obtained; the scoliosis area acquisition module acquires a clustered two-dimensional diagram by using a density-based spatial clustering algorithm and a point cloud reprojection algorithm, and acquires an area of interest by using an image morphological processing algorithm and a polynomial curve fitting method; the point cloud registration module obtains symmetrical point clouds by using an iterative reweighted least square method and a principal component analysis algorithm, performs coarse registration by using a Rodrigues rotation matrix and then executes a point cloud registration algorithm; and the transverse asymmetry analysis module obtains a symmetry plane by using the registered point cloud, and obtains a transverse depth difference point cloud by using a surface reconstruction algorithm. The invention provides a transverse asymmetry analysis method based on a point cloud registration algorithm and a surface reconstruction algorithm, scoliosis screening is performed through the depth camera, and popularization of scoliosis screening is facilitated.

The invention discloses a speedometer design method, system and equipment based on fusion of a wheel speed meter and an IMU (Inertial Measurement Unit). The method comprises the following steps: acquiring a plurality of data measured by an inertial measurement unit, and acquiring an attitude rotation matrix obtained by sequentially rotating a coordinate system b in the inertial measurement unit according to the directions of a Z axis, an X axis and a Y axis; determining a state space model of the odometer; constructing a speed observation model of the wheel speed meter based on the state space model; calculating a first error state of the speed observation model, and determining an observation matrix of the wheel speed meter according to the first error state; inputting a plurality of data measured by the inertial measurement unit into the state space model to obtain a predicted value output by the state space model; and calculating a second error state of the state space model, and determining the state of the odometer based on the state space model, the second error state, the predicted value and the observation matrix of the wheel speed meter. According to the invention, the accuracy and stability of track plotting can be improved.

The invention discloses a surveying and mapping method and system based on an unmanned aerial vehicle remote sensing technology, and relates to the field of surveying and mapping. Comprising the following steps: unmanned aerial vehicle multispectral image data acquisition: carrying a multispectral camera at the bottom of an unmanned aerial vehicle, setting flight parameters of the unmanned aerial vehicle, and acquiring image data by the multispectral camera in the flight process of the unmanned aerial vehicle; image processing: carrying out radiation model correction and geometric correction on the acquired image so as to eliminate illumination variation and geometric distortion and ensure that the physical significance of image data is accurate; performing multi-view image feature matching, establishing a corresponding relation among different view images, and providing matching point pairs for three-dimensional reconstruction; and three-dimensional point cloud reconstruction: restoring a scene three-dimensional structure from the two-dimensional image, and generating a dense point cloud model. The method has the effect of improving precision, centroid centralization eliminates accumulative errors, covariance matrix construction accurately captures point cloud space distribution characteristics, singular value decomposition strictly ensures orthogonality constraint of a rotation matrix, and mathematical derivation can obtain a globally optimal solution.

The invention discloses an IMU (inertial measurement unit) attitude estimation method based on rolling window variance detection, which comprises the following steps: acquiring acceleration data and gyroscope data from an inertial measurement unit, the acceleration data being used for providing gravity direction information, and the gyroscope data being used for providing angular velocity information of a sensor; generating a rotation matrix by using gyroscope data, and calculating an Euler angle based on the rotation matrix; attitude estimation is performed by fusing an Euler angle generated from gyroscope data and an attitude angle calculated from gravitational acceleration. According to the method, a rolling window variance detection mechanism is adopted, the vibration degree of the environment where the IMU is located can be detected in real time, the weight of acceleration data is dynamically adjusted according to the vibration strength, and when it is detected that the vibration is high, the influence of accelerometer data in attitude estimation is reduced, so that the interference of the vibration on attitude estimation is reduced; under the condition of weak vibration, the weight of the acceleration data is increased, and the accuracy and stability of attitude estimation are improved.

The invention discloses an indoor scene point cloud blocking method, and relates to the technical field of point cloud processing. Comprising the steps of obtaining sparse point cloud data; calculating a first rotation matrix and an optimal second rotation matrix based on the sparse point cloud data; inputting single-frame point cloud data frame by frame, aligning the single-frame point cloud data to a Manhattan coordinate system through the first rotation matrix and the optimal second rotation matrix, and creating a sparse voxel grid; calculating a normal vector of each voxel center point based on the sparse voxel grid to obtain a normal vector set of the voxel center points; segmenting the sparse voxel grid by adopting an adaptive segmentation algorithm to obtain an initial point cloud block and recording a boundary value of the initial point cloud block; and according to the normal vector of each voxel central point and the boundary value of the initial point cloud block, expanding the initial point cloud block by using a region growing algorithm to obtain a point cloud block with a complete boundary target. The technical problem that a computer is difficult to read massive point cloud data is solved.

The invention belongs to the technical field of pedestrian navigation and positioning, and relates to a pedestrian inertial navigation method based on action recognition. The method comprises the following steps: collecting IMU data and muscle electricity data by using a wearable device; in a static state, calculating an Euler angle and solving a coordinate rotation matrix by means of IMU data, converting an acceleration in a carrier coordinate system into a navigation coordinate system, and obtaining speed information after integration; after the muscle electricity data is subjected to preprocessing and feature extraction, an SVM model based on statistics and an action correction algorithm is used for recognizing the motion state of the human body in a mixed motion mode, and an action recognition result is obtained; the action recognition result is used for self-adaptively adjusting threshold detection of the zero speed interval, finally, the zero speed is corrected in the zero speed interval through a speed correction method based on the EKF, integration is conducted on the corrected speed, and therefore position information is obtained. According to the method, high positioning precision, small positioning error and good robustness can be realized in a mixed motion mode, and an accurate track can be drawn.

The invention provides a real-time binocular RGB-D camera image splicing method based on three-dimensional geometric properties, and efficient splicing of depth images is realized by accurately estimating a spatial relative relationship between cameras. Firstly, a binocular RGB-D camera is used for shooting a target plane to obtain multiple groups of data; then, extracting a target plane in the point cloud, and calculating a rotation matrix and a translation vector between cameras to obtain a spatial transformation matrix; thirdly, mapping a right camera depth map to a left camera plane through calculation mapping to realize depth value unification; and finally, through feature extraction and geometric correspondence establishment, optimizing splicing fusion parameters, and completing RGB-D image splicing. The method does not need a calibration plate, adapts to different illumination conditions, and has a wide application prospect.