420 results about "Plane fitting" patented technology

The invention relates to the technical field of machine vision and detection, in particular to a shoe pattern defect detection system based on the visual technology, which comprises an acquisition processing unit, a three-dimensional model unit, a defect detection unit and a defect processing unit. The three-dimensional model unit reconstructs an accurate shoe pattern three-dimensional model in sequence, the defect detection unit divides the model into a plurality of areas, calculates the average curvature, and accurately analyzes and judges the flatness defect degree by adopting a plane fitting algorithm based on a least square method and a curvature difference algorithm based on a sliding window; and the defect processing unit automatically processes the shoes with serious defects, so that high efficiency, accuracy and automation of shoe pattern defect detection are realized, and the quality detection level of shoe production is powerfully improved.

The invention provides a regeneration wafer defect detection method and system based on point cloud, and belongs to the technical field of semiconductor manufacturing detection, and the method comprises the steps: carrying out the registration and fusion processing of the point cloud data of a target wafer, and obtaining the target point cloud data; performing plane fitting on the target point cloud data to determine a reference plane; performing feature extraction on the target point cloud data relative to the reference plane to obtain feature data; positioning a defect area based on the feature data, and calculating geometric parameters of the defect area; correcting the geometric parameters by using a preset error compensation model to obtain target geometric parameters; inputting the position of the defect area, the target geometric parameter and the feature data of the corresponding defect area into a defect classification model to obtain a defect type of the defect area; and generating a defect detection result of the target wafer based on the defect types, the positions, the target geometric parameters and the feature data of all the defect areas of the target wafer. According to the invention, the efficiency and reliability of regenerated wafer defect detection are improved.

The invention discloses a geological disaster deformation monitoring method based on an airborne laser radar, and belongs to the technical field of geological disaster monitoring and early warning. The monitoring method comprises the following steps: acquiring ground point cloud data of a target area in different periods through an unmanned aerial vehicle airborne laser radar measurement system; filtering the ground point cloud data of the target area in different periods to obtain real ground point cloud data of the target area in different periods; the real earth surface point cloud data of the target area in different periods are subjected to the following processing: generalization processing; based on a point cloud elevation difference algorithm of plane fitting and space fitting, calculating change conditions of real earth surface point cloud data of the target area in different periods according to a time sequence; and finally obtaining deformation areas of the target area in different periods according to the change conditions of the real earth surface point cloud data of the target area in different periods. The method is high in precision, is more comprehensive, and provides data support for monitoring and early warning of geological disasters.

The invention discloses a high-precision plane fitting method for a multi-noise point cloud, and the method comprises the steps: carrying out the plane rough fitting of an original point cloud after down-sampling, taking the longest side of a minimum bounding box of a lower sampling point cloud as a distance threshold value, and calculating the distance weight of each point through a weight mathematical model, then, plane fine fitting is carried out on the weighted down-sampling point cloud, the distance weight of each point is updated after each fitting, plane fitting is carried out on the point cloud of which the weight is updated again, and a fine fitting plane of the down-sampling point cloud is calculated in an iterative manner; and finally, calculating the distance weight of each point in the original point cloud based on the fine fitting plane and the iterated distance threshold value, performing plane fitting on the weighted original point cloud in an iteration mode, and finally obtaining the fine fitting plane of the original point cloud. According to the technical scheme, the method is high in stability and low in calculation amount, can obtain a high-precision fitting plane in a weighted iteration mode under the condition of more noise, and is suitable for different types of point cloud data.

The invention relates to the technical field of computer vision detection, and discloses a height feature information generation method, device and equipment based on point cloud and a medium, and the method comprises the steps: obtaining three-dimensional point cloud data of the surface of an object to be detected, carrying out the preprocessing of the data to obtain a target point cloud, and carrying out the reference plane fitting of the target point cloud, and calculating the vertical distance of each point in the target point cloud based on the reference plane obtained by fitting and generating height difference data, and finally extracting and outputting surface height feature information according to the height difference data. According to the method, the three-dimensional point cloud data is subjected to preprocessing noise removal, datum plane accurate fitting and height difference data separation, high-precision extraction of tiny height changes of a complex surface is achieved, the detection efficiency is improved, dependence on high-cost optical equipment is reduced, and the method is compatible with various workpiece shapes.

The invention relates to a workpiece polishing track generation method and equipment, and the method comprises the steps: collecting a current workpiece surface point cloud, carrying out the plane fitting, obtaining an upper surface equation and an upper surface point cloud, projecting the upper surface point cloud to the upper surface equation, obtaining a two-dimensional projection drawing, obtaining the template workpiece contour of each type of template workpiece, and carrying out the polishing of the workpiece. And calculating the polishing track points of each type of template workpiece, matching the two-dimensional projection drawing with the contour of the template workpiece to obtain the corresponding polishing track points, and back-projecting the polishing track points to the three-dimensional space to obtain the three-dimensional polishing track of the current workpiece. The problem that the workpiece surface polishing track cannot be quickly and effectively recognized in the prior art is solved.

The invention relates to a method for measuring the spatial pointing precision of a panoramic camera turntable, which comprises the following steps of: firstly, establishing an initial coordinate system for an azimuth axis and a pitch axis, performing data measurement on the two axes during rotary motion through a laser tracker, and calculating circle center coordinates and circular surface direction vectors of the two rotary axes through a spatial circle fitting method of sphere fitting and plane fitting; the actual position relation of the two coordinate systems can be obtained, then the offset and the angular deviation between the two coordinate systems can be calculated through the actual position relation of the two coordinate systems, and calibration parameters are obtained; and solving a transformation matrix between the reference coordinate system and the measurement coordinate system by using the actual position relation of the two coordinate systems, obtaining a coordinate vector of a measured point in the reference coordinate system, and solving a space included angle between the two vectors according to the coordinate vector and a theoretical position coordinate vector of the measured point to obtain a space pointing error. The method adopts a space coordinate calculation mode of a robotization method, not only has universal significance, but also can be popularized to precision measurement of other two-axis universal two-dimensional space pointing mechanisms.

The invention belongs to the technical field of plate shape monitoring and visual analysis, and discloses a stainless steel medium-thickness plate double-face contour measurement method and system, and the method comprises the steps: S1, completing the calibration of a double-line-scan digital camera, and obtaining the upper and lower surface images of a medium-thickness plate; s2, carrying out binocular stereoscopic vision three-dimensional reconstruction to generate a point cloud picture, and converting the point cloud picture into a point cloud picture under the same coordinate system for display; s3, eliminating transverse rotation and deviation according to point cloud center line fitting and plane fitting of the upper surface and the lower surface of the medium-thickness plate; s4, comparing the geometrical relationship between the point clouds of the upper and lower surfaces to quantify local vibration and thickness change, correcting vibration errors, and obtaining the thickness of the medium-thickness plate; and S5, extracting an edge point set from the point cloud image to obtain the overall contour of the medium-thickness plate, and obtaining the position of a shear line based on the width and edge linearity detection of the medium-thickness plate. By the adoption of the technical scheme, the overall contour, overall thickness distribution and accurate shearing position of the medium-thickness plate can be obtained.

The invention discloses a laser radar visual joint calibration method based on lane line extraction, and the method comprises the steps: S1, obtaining environment point cloud data from a laser radar sensor, carrying out the plane fitting through an RANSAC algorithm, carrying out the lane line point cloud extraction, and carrying out the clustering of the extracted lane line point cloud, and forming a line feature point cloud; s2, acquiring environment image data from a camera, performing image semantic segmentation on the environment image data, extracting a pixel region of a lane line in the environment image data by using a DeepLab-V3 network, and then performing feature mask binarization processing; s3, finding out line features in the environment point cloud data and the environment image data, then carrying out consistency matching, solving a three-line perspective problem, and obtaining preliminary external parameter estimation by establishing a corresponding relation of three line features; and S4, performing refined calibration on the preliminary matching result by using an optimization algorithm, and outputting external parameter calibration results of the laser radar and the camera through multiple measurements and data fusion, including a rotation matrix and a translation vector.

The invention provides a solid-state laser radar and IMU (Inertial Measurement Unit) robust tight coupling system under the assistance of point cloud intensity, which comprises the following steps of: firstly, pre-processing a solid-state laser radar point cloud by the system, eliminating point cloud defect points, correcting a point cloud intensity value and performing down-sampling; and then geometric feature extraction and intensity feature extraction are carried out on the preprocessed point cloud, and intensity calibration of the laser radar point cloud is carried out. And then compensating the motion of the point cloud by using the high-frequency prior information predicted by the IMU. For geometric and intensity feature point clouds, a classical registration method and a fast plane fitting method are respectively adopted to calculate residual errors, and an intensity feature assisted point cloud residual error measurement model is constructed. And a robust strategy based on an IGGIII robust weight function is designed by using a residual error estimation value in a filtering algorithm. According to the invention, on the premise of keeping relatively low calculation cost, the positioning and mapping capabilities and robustness of the small-view-angle laser radar tight coupling system in a complex environment are improved.

The invention discloses a building roof elevation extraction method and system based on city modeling, and the method comprises the steps: S1, point cloud data and image data fusion registration: extracting data features, and carrying out alignment registration; s2, roof denoising and boundary optimization: identifying interference information, extracting a roof boundary, and generating a roof digital surface model through interpolation; s3, roof elevation extraction: for a flat roof, extracting the top elevation of the flat roof through a plane fitting method; for the slope top roof, extracting the top elevation of the slope top roof through an extreme point detection method; and extracting the top elevation of the roofs except the flat roofs and the slope roofs through a point cloud clustering method. According to the scheme, the roof elevation can be rapidly and accurately extracted, and high-precision three-dimensional data support is provided for urban planning, energy assessment and disaster emergency; the problems that in the prior art, roof elevation extraction efficiency is low, complex roof structure recognition precision is insufficient, and multi-source data fusion is difficult are solved.

The invention relates to the technical field of three-dimensional modeling, in particular to a three-dimensional modeling system and method based on a laser radar, and the system comprises a point cloud boundary extraction module, a boundary error analysis module, a self-adaptive cutting repair module and a stitching structure optimization module. According to the method, three-dimensional coordinates, normal vectors, reflection intensity, distances and indexes of boundary points are called layer by layer, a Sobel operator and a Canny algorithm are combined to cooperatively recognize a boundary and an error region, then local region repair is completed based on least square plane fitting and spatial residual analysis, a tension vector is analyzed through dynamic planning, and stitching path adjustment is executed. On the cooperative basis of multi-type point cloud features, a spatial topological structure and boundary continuity dynamic modeling, the problems of local damage, connection errors and instable stitching paths of point cloud boundaries in complex scenes are solved, and the integrity of boundary structures, the coherence of stitching transition and the rationality of spatial geometric expression are remarkably improved.

The invention discloses a wind driven generator blade detection method, system and device and a storage medium. First point cloud data of a target blade are acquired through a laser radar carried by an unmanned aerial vehicle; performing plane fitting and grid map conversion on the first point cloud data to obtain a fitting plane and a grid map; scanning and querying the grid map to obtain a plurality of blade occupation points, and clustering the blade occupation points to obtain blade positions; determining a hub position according to the blade position, and obtaining a blade angle according to the hub position and the blade position; and updating the orbital motion trajectory of the unmanned aerial vehicle according to the fitting plane and the hub position, and returning to the step of obtaining the first point cloud data of the target blade through the laser radar carried by the unmanned aerial vehicle until the currently obtained blade angle and the blade angle obtained in the last round meet a preset convergence condition. The method improves the accuracy of wind driven generator blade angle detection, also improves the quality of wind driven generator blade images, and can be applied to the technical field of machine vision.

The invention relates to the technical field of building structure health monitoring, in particular to an unmanned aerial vehicle laser radar-based building point cloud deformation quantitative extraction method, which comprises the following steps of: based on building corner structure characteristics in point cloud data, carrying out quantitative evaluation on the overall inclination state of a building through point cloud segmentation, straight line fitting and angle calculation; based on an ideal plane hypothesis, performing plane fitting and point-to-plane distance analysis on the building facade point cloud in the point cloud data, identifying a local concave-convex deformation area of the building facade, and performing quantitative evaluation; and roof plane detection and wall surface normal vector analysis are carried out based on the point cloud data, and accurate quantification is carried out on parapet wall inclination based on region growth segmentation and PCA principal direction analysis. Through automatic processing and quantitative analysis of an algorithm, subjective errors and inconsistency possibly occurring in a traditional manual evaluation method are avoided, and objectivity and repeatability of an evaluation result are enhanced.

The embodiment of the invention discloses a robust extraction method for three-dimensional geometric features of a precast beam based on point cloud edge topology recombination. According to the technical scheme provided by the embodiment of the invention, the three-dimensional point cloud data of the precast beam is subjected to the surface domain separation to obtain the surface domain separation result, the plane fitting is performed according to the surface domain separation result to obtain the fitting plane, and the three-dimensional point cloud data is projected to the fitting plane to obtain the two-dimensional projection point cloud data; edge point detection is performed on the two-dimensional projection point cloud data to obtain a precast beam edge point set, ordered recombination is performed on the precast beam edge point set to obtain an ordered edge point sequence, and feature corner point extraction is performed according to the ordered edge point sequence to obtain precast beam corner points, so that the precast beam corner point extraction efficiency, accuracy and robustness are improved; the precast beam feature extraction efficiency can be improved, and the precast beam feature extraction precision can be effectively improved.

The invention relates to the technical field of large-size part three-dimensional measurement, and discloses a large-size part three-dimensional size measurement method and device based on machine vision, and the method comprises the steps: obtaining a material reflection characteristic reference data set and original point cloud data; according to the reference data set, laser power and gain are adjusted in a self-adaptive mode, and optimized scanning parameters are obtained; and performing denoising, feature extraction, region segmentation and plane fitting according to the optimized parameters and the original point cloud to obtain a high-precision geometric dimensioning result. According to the invention, high-precision, high-stability and high-efficiency three-dimensional size measurement can be realized on a large-size part made of a complex material and having a curved surface, and the three-dimensional size measurement method is obviously superior to a traditional scheme.

The invention discloses a LiDAR-IMU-camera tight coupling positioning and mapping method and device for a mobile platform, and belongs to the technical field of robot SLAM. Synchronously triggering the color camera, the laser radar and the IMU through hardware, and unifying timestamps; performing compensation and distortion removal on the laser point cloud motion by the IMU data; based on curvature, intensity and density self-adaptive downsampling, local plane fitting errors are used for distributing observation weights; the IMU pre-integration pose is used as an initial value, point-to-surface registration of the weighted point cloud and the local map is carried out, and a laser-IMU tight coupling odometer factor is obtained; the color image and the laser intensity graph are fused into a multi-mode loopback descriptor, and loopback factors are generated through global retrieval and geometric verification; and inputting an odometer, an IMU, a laser loopback factor and a visual loopback factor into an increment factor graph optimizer, jointly solving a global optimal key frame pose, and outputting a dense laser point cloud map and a color visual point cloud map. The method can be operated in real time on an embedded platform, effectively inhibits drifting, and realizes centimeter-level global consistent positioning and mapping.

The invention provides a crack three-way measurement research method and system based on multi-coordinate system fusion, and relates to the technical field of earth and rockfill dam monitoring. The method comprises the following steps: shooting concentric circle target images at multiple angles through a monocular camera, extracting feature points through preprocessing, and correcting projection distortion of non-parallel shooting; establishing a mapping relation between pixels and a three-dimensional coordinate system, and optimizing camera pose parameters; reconstructing a three-dimensional coordinate point cloud based on the multi-view data, screening outliers and fitting plane optimization data; and registering the three-dimensional point cloud to a world coordinate system through a coordinate system conversion algorithm, and comparing two measurement results to calculate the three-way displacement of the crack. According to the method, concentric circle target constraint feature precision is utilized, projection correction and plane fitting are combined to compensate monocular vision depth deficiency, high-precision displacement detection is achieved, and engineering monitoring requirements are met.

The invention discloses an indoor scene three-dimensional reconstruction method based on deep fusion and confidence modeling, and belongs to the technical field of computer vision. According to the method, composite data frames such as a color image, a depth map, an IMU (Inertial Measurement Unit) and a camera attitude are comprehensively utilized to carry out regional three-dimensional reconstruction on an indoor scene: firstly, the scene is divided into a smooth region (such as a wall, a ground, a ceiling, a glass plane, a mirror surface, a blackboard and other planes) and a complex curved surface region; aiming at the smooth area, adopting geometric prior guide plane fitting provided by a visual large model, and combining sensor attitude information to quickly reconstruct a regular plane model; for a complex curved surface area, a multi-frame point cloud fusion strategy is adopted to accumulate different view angle information, and a deep residual error refining network is utilized to recover curved surface details, so that a high-precision curved surface model is obtained. According to the method, the three-dimensional structure of the indoor scene can be efficiently reconstructed, the global framework of the smooth area is reserved, and the details of the surface of a complex object are depicted in detail.

The invention relates to the technical field of underground engineering tunnel face information extraction, and discloses an information extraction method for an underground engineering tunnel face rock mass structural plane, and the method comprises the steps: extracting the normal vector of each point from the original point cloud data of the underground engineering tunnel face rock mass structural plane, inputting the converted normal vector into a BIRCH algorithm for clustering to obtain a dominant structural plane category; taking the types of the dominant structural surfaces as input, executing a DBSCAN algorithm on the point cloud of each type of dominant structural surfaces, and segmenting the dominant structural surfaces into independent structural surfaces; performing plane fitting on the independent structural plane by using an RANSAC algorithm to obtain a plane fitting equation, and calculating an inclination angle and an inclination direction of the structural plane according to the plane fitting equation; according to the method, point cloud data provides a rich information source for rock mass structure analysis, an independent structural plane is accurately recognized, and tunnel face rock mass structural plane information is reasonably extracted.

The invention provides a material box pose recognition method and system, a storage medium and a robot, and the method comprises the steps: collecting scene image data and point cloud data, and building a mapping relation; rGB pixels belonging to the feature surface of the workbin in the image data are identified, and a corresponding workbin point cloud is obtained according to the mapping relation; performing plane fitting processing on the point cloud of the workbin to determine a plane normal direction so as to estimate a rotation angle yaw of the workbin; the center P'of the material box is roughly positioned, and plane point clouds in the plane area are screened out based on yaw and the specification of the material box; projecting the plane point cloud into a binary image, screening out the outline of the material box, and carrying out internal area filling treatment; and calculating the center of mass of the internal area of the workbin contour, and estimating the position of the scene space where the center of the workbin is located according to the position and the projection relation of the pixels of the center of mass. Therefore, the posture of the material box is estimated by identifying the shape characteristics of the material box.

The invention provides a coal mine roadway point cloud data simplification method and device, a storage medium and electronic equipment, and the method comprises the steps: constructing a point cloud topological structure for roadway original point cloud data through a kd tree, setting a domain radius, selecting core points, recording index values, and then estimating a normal vector of each core point through a principal component analysis method and a plane fitting method; the method comprises the following steps: constructing a cylinder field at a core point by using a normal vector, then accelerating the search of a ball field of the core point by using a kd tree, calculating whether each point in the ball field is a point in the cylinder field, finally calculating the center of mass of the points in the cylinder field, and keeping the point closest to the center of mass, so that a constructed new point cloud is a simplified point cloud. The thickness of the point cloud is reduced while the point cloud is simplified, so that the problem that an existing roadway point cloud simplification method does not consider the thickness of the point cloud during point cloud simplification and cannot meet the precision requirement of similar deformation monitoring based on the roadway point cloud is solved.

The invention relates to the technical field of building design, in particular to a BIM building model construction method and system.The BIM building model construction method comprises the steps that firstly, based on the plane continuity characteristic of a wall body structure in a building construction environment, rough segmentation is conducted on point cloud through an RANSAC multi-plane fitting algorithm, and a wall area and outer points of a complex structure are preliminarily separated; further, for complex structure areas such as curved surfaces and arc connection areas, model contribution factors are dynamically generated by calculating relative distances and geometric coherence between outer points and a fitting plane, and higher weights are given to point clouds in a high-curvature area; according to the method, the registration process is optimized in combination with the weighted ICP algorithm, and the key structure area is aligned preferentially, so that the damage to the integrity and continuity of a complex structure is reduced, the registered building point cloud data after registration is more accurate, and the accuracy of the BIM model constructed according to the registered building point cloud data is improved.

The invention provides an urban special-shaped building monomer reconstruction method based on inclination model data, and the method comprises the steps: carrying out the segmentation and recognition of a monomer building based on deep learning, European clustering and a graph cutting method, fitting a feature plane of the monomer building based on a point cloud plane fitting algorithm, recognizing a complex special-shaped building according to the normal vector gradient distribution of the feature plane, and carrying out the reconstruction of the complex special-shaped building. According to the method, the three-dimensional Mesh model of the special-shaped building is simplified, textures are filled and the like, reconstruction of a complex special-shaped building model is achieved, and the problems that an existing mainstream urban building monomer reconstruction algorithm is only suitable for common regular buildings, and automatic monomer segmentation and three-dimensional structured reconstruction of the special-shaped building cannot be achieved are solved. And the automation rate and the efficiency of reconstruction of the special-shaped building in the urban scene are obviously improved.

The invention discloses a target sheet identification method based on point cloud data, and belongs to the field of target sheet identification methods, and the method comprises the following steps: 1, collecting original point cloud data of a target range environment through a laser radar; step 2, anti-rain and anti-fog interference processing; step 3, carrying out gale shake compensation; 4, reconstructing a basic curved surface, and executing random sampling consistency plane fitting; step 5, enhancing and reconstructing a wrinkle region, specifically, detecting a region with a curvature change rate of greater than 0.05 mm <-2 > on the basic curved surface; marking as a to-be-enhanced area and transmitting to a sub-curved surface processing flow; step 6, bullet hole identification: calculating the local curvature of the point cloud, and screening abrupt change points of which the curvature values are greater than 0.25 mm <-1 >; fitting the three-dimensional coordinates of the bullet hole by adopting a least square method; and step 7, outputting target sheet attitude parameters and bullet hole ring values. According to the invention, interference of rain, fog and strong wind can be reduced, and identification precision is improved.

The invention discloses an attitude detection method and system for a photovoltaic panel. The method comprises the following steps: S1, scanning through a laser radar to obtain light point cloud data; s2, segmenting the point cloud data through a clustering algorithm, and determining and storing clustering point cloud data to which the photovoltaic panel belongs; s3, performing plane fitting on the clustering point cloud data to which the photovoltaic panel belongs to obtain a plane equation, and projecting the point cloud data to the plane equation; s4, performing edge extraction on the point cloud of the photovoltaic panel to obtain a top edge linear equation; s5, postures are combined according to the vectors. According to the invention, the laser radar is used for data acquisition, the stability of equipment is improved, the operation is simple, other sensors are not depended on, a training set is not needed to train a model, the use stability and reliability are high, the system can be used in extreme environments such as night, heavy fog and rainy days, and the real-time analysis effect is achieved through algorithm optimization of point cloud processing. And the calculation efficiency of the attitude of the photovoltaic panel is improved.

The invention provides a pallet pose estimation method and system, and the method comprises the steps: obtaining a color image which comprises a pallet region and at least part of pixels of which are aligned, and point cloud data, and cutting the color image to obtain a target image which only comprises the pallet region; obtaining a point cloud of an area corresponding to the target image from the point cloud data according to an alignment relationship between the color image and the point cloud data, and obtaining a to-be-fitted point cloud; performing plane fitting on the to-be-fitted point cloud to obtain a plane fitting model; angle information and center coordinates of the pallet are calculated based on the plane fitting model and a preset reference coordinate system, and the angle information and the center coordinates of the pallet are utilized to construct a pose matrix of the pallet to obtain pose information of the pallet. The pallet pose estimation method provided by the invention has higher robustness and higher accuracy.

The invention discloses a reinforcing steel bar surface binding point identification and positioning method based on point cloud and pixel mapping. The method comprises the following steps: obtaining original three-dimensional point cloud data to carry out outlier noise point elimination; performing plane fitting on the denoised point clouds, identifying template surface point clouds and removing the template surface point clouds; mapping the spatial physical coordinate attribute of the steel bar surface point cloud into a three-channel coding image; separating the three-channel coded image into three single-channel images: a length image, a width image and a depth image; processing the depth image to extract an initial reinforcing steel bar straight line set; screening out a parallel steel bar straight line set from the initial steel bar straight line set according to a set reference direction and a parallel angle tolerance threshold value; and calculating straight line intersection points in the parallel steel bar straight line set to obtain candidate binding point pixel coordinates, determining representative binding point pixel positions through clustering, querying coordinate information of the three single-channel images, and outputting three-dimensional space coordinates of the steel bar binding points. According to the invention, robust and accurate steel bar binding point identification and positioning are realized.

The invention relates to a method for detecting the gradient of a plane and optimizing the machining precision, and the method comprises the following steps: (1), carrying out the preliminary path collection processing of the plane at the current position before the actual machining; (2) carrying out optimal feature point selection based on the acquired data set; (3) carrying out plane fitting processing based on the selected optimal feature point; (4) calculating a normal vector of the current position plane and a corresponding base vector based on the selected optimal feature point; (5) constructing a local coordinate system based on the current position, and calculating transformation between the local coordinate system and the workpiece global coordinate system; and (6) optimizing the machining path of the cutting head according to the local coordinate system. By adopting the method for detecting the inclination of the plane and optimizing the machining precision, the machining path is optimized according to the local coordinate system, so that the machining precision is effectively improved, and errors caused by inclination of the workpiece are reduced.

The invention relates to a seedling feeding and discharging robot greenhouse autonomous navigation method and device and electronic equipment, and the method comprises the steps: obtaining multi-source sensor data and semantic information of a breeding workshop, and determining a semantic coarse positioning position according to environment characteristics, so as to form a preliminary semantic positioning frame; based on a ground segmentation and plane fitting algorithm, the relative position relation between the robot and the ground is determined, and the relative position relation comprises the pitch angle, the yaw angle and the offset in the Z-axis direction; deeper feature extraction is carried out on details of the surrounding environment by combining a semantic coarse positioning position and local sensing data collected by a robot in real time; and on the basis of a grid projection method, carrying out accurate matching on local structural features to generate a global pose of the robot relative to the point cloud map, and determining a path plan. Through the multi-source data fusion technology, it is conveniently ensured that the robot can conduct accurate movement, obstacle avoidance and path planning in the complex environment, and collision or misoperation is avoided.