916 results about "Binocular stereo" patented technology

The invention discloses a parallax optimization algorithm-based binocular stereo vision automatic measurement method. The method comprises the steps of 1, obtaining a corrected binocular view; 2, matching by using a stereo matching algorithm and taking a left view as a base map to obtain a preliminary disparity map; 3, for the corrected left view, enabling a target object area to be a colorized master map and other non-target areas to be wholly black; 4, acquiring a complete disparity map of the target object area according to the target object area; 5, for the complete disparity map, obtaining a three-dimensional point cloud according to a projection model; 6, performing coordinate reprojection on the three-dimensional point cloud to compound a coordinate related pixel map; 7, using a morphology method to automatically measure the length and width of a target object. By adopting the method, a binocular measuring operation process is simplified, the influence of specular reflection, foreshortening, perspective distortion, low textures and repeated textures on a smooth surface is reduced, automatic and intelligent measuring is realized, the application range of binocular measuring is widened, and technical support is provided for subsequent robot binocular vision.

The invention discloses a binocular stereo vision three-dimensional measurement method based on line structured light scanning, which comprises the steps of performing stereo calibration on binocularindustrial cameras, projecting laser light bars by using a line laser, respectively acquiring left and right laser light bar images, extracting light bar center coordinates with sub-pixel accuracy based on a Hessian matrix method, performing light bar matching according to an epipolar constraint principle, and calculating a laser plane equation; secondly, acquiring a line laser scanning image of aworkpiece to be measured, extracting coordinates of the image of the workpiece to be measured, calculating world coordinates of the workpiece to be measured by combining binocular camera calibrationparameters and the laser plane equation, and recovering the three-dimensional surface topography of the workpiece to be measured. Compared with a common three-dimensional measurement system combininga monocular camera and line structured light, the binocular stereo vision three-dimensional measurement method avoids complicated laser plane calibration. Compared with the traditional stereo vision method, the binocular stereo vision three-dimensional measurement method reduces the difficulty of stereo matching in binocular stereo vision while ensuring the measurement accuracy, and improves the robustness and the usability of a visual three-dimensional measurement system.

The invention discloses a binocular stereo vision based intelligent three-dimensional human face rebuilding method and a system; the method comprises: preprocessing operations including image normalization, brightness normalization and image correction are carried out to a human face image; a human face area in the human face image which is preprocessed is obtained and human face characteristic points are extracted; the object is rebuilt by projection matrix, so as to obtain internal and external parameters of a vidicon; based on the human face characteristic points, gray level cross-correlation matching operators are expanded to color information, and a parallax image generated by stereo matching is calculated according to information including polar line restraining, human face area restraining and human face geometric conditions; a three-dimensional coordinate of a human face spatial hashing point cloud is calculated according to the vidicon calibration result and the parallax image generated by stereo matching, so as to generate a three-dimensional human face model. By adopting the steps, more smooth and vivid three-dimensional human face model is rebuilt in the invention.

The invention provides a calibration method for a binocular stereo measurement system, and belongs to the technical field of measurement and test. The invention adopts a plane calibrating board having a characteristic dot array with an accurate given distance between the centers of two characteristic dots; the calibrating board is shot from more than three angles; initial values of the intrinsic and extrinsic parameters of two cameras are solved by using the linear method under a pinhole model; the distortion effect is considered; the intrinsic and extrinsic parameters of the two cameras are taken as optimization variables for a first optimization; the space coordinates of the centers of the characteristic dots on the calibrating board are taken as optimization variables for a second optimization; the translation vector between the cameras is scaled to the ratio of the calculated distance to the given accurate distance, thus getting a calibration result of the parameters; the result of the second optimization is got by using the intrinsic parameters and the relative attitude parameters of the two cameras. Considering the influence of the geometrical errors of the calibrating board, the method reduces the manufacturing, measurement and calibration requirement of the calibrating board and gets calibration result of high precision.

The invention discloses a cucumber picking robot system in a greenhouse environment. The robot system comprises a binocular stereo vision system, a mechanical arm device and a robot mobile platform; the binocular stereo vision system is used for acquiring cucumber images, processing the images in real time and acquiring the position information of the acquired targets; the mechanical arm device is used for capturing and separating the acquired targets according to the position information of the acquired targets; and the robot mobile platform is used for independently moving in the greenhouse environment; wherein, the binocular stereo vision system comprises two black and white cameras, a dual-channel vision real-time processor, a lighting device and an optical filtering device; the mechanical arm device comprises an actuator, a motion control card and a joint actuator; and the robot mobile platform comprises a running mechanism, a motor actuator, a tripod head camera, a processor and a motion controller. The invention also discloses a cucumber picking method in the greenhouse environment. The method of combining machine vision and agricultural machinery is adopted to construct the cucumber picking robot system which is suitable for the greenhouse environment, thus realizing automatic robot navigation and automatic cucumber reaping, and reducing the human labor intensity.

The invention discloses an omni-directional automatic forklift and a 3D stereoscopic vision navigating and positioning method. The 3D stereoscopic vision navigating and positioning method comprises the following steps: acquiring information of a 3D coordinate in the working environment of the automatic forklift; generating a 3D map; acquiring real-time images and positioning an initial position according to the 3D map; determining a target position; navigating to the target position and judging whether encountering barriers; scanning and identifying two-dimensional codes on a goods shelf; judging whether the automatic forklift is over against the goods and is aligned with the goods; enabling the automatic forklift to insert into a goods shelf tray and completing taking the goods and putting the goods. The omni-directional automatic forklift and the 3D stereoscopic vision navigating and positioning method have the benefits that the 3D map of the working environment is built by a plurality of binocular stereo cameras, the automatic forklift is capable of effectively and accurately positioning, taking the goods and putting the goods with combination of a speedometer, a laser radar and a front camera, three-freedom-degree omni-directional movement on the plane is implemented by Mecanum wheels, the automatic forklift is capable of taking the goods and putting the goods on the same goods shelf without steering, any barriers in the working environment can be effectively avoided in tine by the laser radar and an infrared sensor, and the automatic forklift can be automatically charged by communication between an upper computer and a charging box.

The invention discloses a binocular stereoscopic vision matching method combining depth characteristics. The binocular stereoscopic vision matching method comprises: obtaining a depth characteristic pattern from left and right images through a convolutional neural network; calculating a truncation similarity measurement degree of pixel depth characteristics by taking the depth characteristics as the standard, and constructing a truncation matching cost function combining color, gradients and depth characteristics to obtain a matched cost volume; processing the matched cost volume by adopting a fixed window, a variable window and a self-adaptive weight polymerization or guide filtering method to obtain a cost volume polymerized by a matching cost; selecting an optimal parallax error of the cost volume by adopting WTA (Wireless Telephony Application) to obtain an initial parallax error pattern; then finding a shielding region by adopting a double-peak test, left-right consistency detection, sequence consistency detection or shielding constraint algorithm, and giving a shielding point to a parallax error value of a same-row point closest to the shielding point to obtain a parallax error pattern; and filtering the parallax error pattern by adopting a mean value or bilateral filter to obtain a final parallax error pattern. By adopting the binocular stereoscopic vision matching method combining the depth characteristics, the incorrect matching rate of three-dimensional matching can be effectively reduced, the images are smooth and image edges including edges of small objects are effectively kept.

The invention discloses a regional depth edge detection and binocular stereo matching-based three-dimensional reconstruction method, which is implemented by the following steps: (1) shooting a calibration plate image with a mark point at two proper angles by using two black and white cameras; (2) keeping the shooting angles constant and shooting two images of a shooting target object at the same time by using the same camera; (3) performing the epipolar line rectification of the two images of the target objects according to the nominal data of the camera; (4) searching the neighbor regions of each pixel of the two rectified images for a closed region depth edge and building a supporting window; (5) in the built window, computing a normalized cross-correlation coefficient of supported pixels and acquiring the matching price of a central pixel; (6) acquiring a parallax by using a confidence transmission optimization method having an acceleration updating system; (7) estimating an accurate parallax by a subpixel; and (8) computing the three-dimensional coordinates of an actual object point according to the matching relationship between the nominal data of the camera and the pixel and consequently reconstructing the three-dimensional point cloud of the object and reducing the three-dimensional information of a target.

A synchronous quick calibration method of a plurality of video cameras in a three-dimensional scanning system, which includes: (1) setting a regular truncated rectangular pyramid calibration object, setting eight calibration balls at the vertexes of the truncated rectangular pyramid, and respectively setting two reference calibration balls at the upper and lower planes; (2) using the video cameras to pick-up the calibration object, adopting the two-threshold segmentation method to respectively obtain the corresponding circles of the upper and lower planes, extracting centers of the circles, obtaining three groups of corresponding relationships between circle center points in the image and the centres of calibration ball in the space, solving the homography matrix to obtain the internal parameter matrix and external parameter matrix and obtaining the distortion coefficient, taking the solved video camera parameter as the initial values, and then using a non-linear optimization method to obtain the optimum solution of a single video camera parameter; (3) obtaining in sequence the external parameter matrix between a plurality of video cameras and a certain video camera in the space, using the polar curve geometric constraint relationship of the binocular stereo vision to establish an optimizing object function, and then adopting a non-linear optimization method to solve to get the optimum solution of the external parameter matrix between two video cameras.

The invention provides a joint measurement method based on a laser radar and a binocular visible light camera to obtain accurate and dense three-dimensional information simply and efficiently. The joint measurement method comprises assuming that the laser radar is a camera device with a fixed internal reference; directly projecting three-dimensional point cloud data into a two-dimensional image, calculating rotation and translation relationships between the laser radar device and the binocular camera by using an image processing method and the matching between the two-dimensional images; creatively introducing an idea for obtaining a matrix norm and a matrix trace to solve a rotation matrix; finally fusing laser radar and binocular stereo vision point cloud data. The method not only can obtain an accurate position and attitude information, but also can reconstruct the special texture and feature information of a target surface, which has a high application value for spacecraft dockingand hostile satellite capture in the military field and workpiece measurement and unmanned driving in the civilian field.

Systems and methods are described for predicting depth from colour image data using a statistical model such as a convolutional neural network (CNN), The model is trained on binocular stereo pairs of images, enabling depth data to be predicted from a single source colour image. The model is trained to predict, for each image of an input binocular stereo pair, corresponding disparity values that enable reconstruction of another image when applied, to the image. The model is updated based on a cost function that enforces consistency between the predicted disparity values for each image in the stereo pair.

The invention discloses a fast 3D face identifying method based on double-eye passive solid sight, which includes the following steps: 1) a non-contact short shaft parallel binocular stereo vision system is built by applying two high-definition digital cameras; 2) after system calibration is finished, face detection and collection based on a haar-AdaBoost sorting machine is carried out on a preview frame image for obtaining corresponding upper and lower stereoscopic vision graph pairs and estimating a sight difference; image correction is carried out on a face area for obtaining the upper and lower stereoscopic vision graph pairs vertical to the polar lines inside and outside the area; 3) the accurate location on the eyes and a spex nasi is captured by applying a Bayesian and the haar-AdaBoost sorting machines as well as point cloud 3D information for building a benchmark triangle; 4) the corresponding sub pixels in the middle and small areas are matched by applying the pyramidal parallel search solid graph of a phase relevant arithmetic based on a complex wavelet; 5) pose normalizing and hole filling are carried out on the faces under different poses by applying the built benchmark triangle; 6) expression normalization is carried out on different faces based on the suppose that the surface geodesic distance of the face is invariable; 7) the 3D faces after normalization are identified by utilizing the arithmetic. The method has the beneficial effects of: mainly solving the problems of being hard to fast and automatically obtain the passive stereoscopic vision and identifying the 3D point cloud information of the dense and accurate face under different poses and expressions, thus leading the 3D face identifying process to be faster, more hidden, safer and more reliable.

The invention discloses a movement capturing method based on multiple binocular stereo vision. A movement video collecting device is constructed, and human movement video sequences from different orientations are collected by the movement video collecting device. Multiocular movement video sequences shot by a plurality of cameras are calibrated. Marked points matching and tracking of each binocular tracker is finished. Data fusion of three-dimensional tracking result of multiple binocular trackers is completed. The three-dimensional movement information of the marked points acquired by a multiocular fusion device is fed back to the binocular tracker to consummate binocular tracking. On the basis of binocular three-dimensional tracking realized by binocular vision, the invention fuses multiple groups of binocular three-dimensional movement data, resolves parameter acquiring problem of three-dimensional position, tracking, track fusion and the like for a plurality of marked points, increases number of traceable market points and enables the tracking effect to be comparable with three-dimensional movement acquiring device employing multi-infrared cameras for collecting.

The invention provides a self-calibration method for a binocular stereo vision device, which comprises the following steps of: firstly, matching corresponding points between two view pictures acquired by the binocular stereo vision device; secondly, realizing projective reconstruction according to the matching relationship of the corresponding points; thirdly, calibrating out a transformation matrix from a projective space to a metric space according to prior constraints of parameters in a camera; and finally, calculating the internal and external parameter matrixes of two cameras. The self-calibration method has the advantages of restoring the internal and external parameter matrixes of the two cameras from the two view pictures by adopting absolute dual quadric-based self-calibration technology; guaranteeing the robustness of algorithm by adopting a ransack method to estimate the basic matrix and select matched feature points obtained by SIFT algorithm; and enabling all the restraints to internal parameters in the calibration to become linear restraints by decomposing the absolute dual quadric so as to avoid solving a non-linear equation.

The invention discloses a spatial circle geometrical parameter dual-eye solid visual measurement method suitable for non-contact type online measurement of the spatial circle geometrical parameter of an industrial product, aiming at overcoming the existing problems of low measurement precision, slow measurement speed and low automation degree. The method is divided into a calibrating stage, an image processing stage and a spatial circle fitting stage. A neural network technique is firstly adopted to carry out the calibration of a camera; a dual-camera is utilized to carry out the extraction of sub-pixel image edges of the spatial circle, a simple algorithm is developed based on the image gray gradient distribution characteristic so as to realize the match of edge points, the practical 3D spatial coordinates of the circle edges are then obtained, and the geometrical parameters of the spatial circle comprise radius, position of center of circle and orientation of the plane where the spatial circle is arranged are gained by the fitting of the spatial circle with corresponding mathematical geometrical knowledge. The spatial circle geometrical parameter dual-eye solid visual measurement method has fast image processing speed and high automation degree, and has relative measurement error of the spatial circle of superior to plus or minus 0.6% when the plane where the spatial circle is arranged has an angle of about 50 DEG with the image plane.

The invention discloses a ground obstacle detection method based on the binocular stereo vision of a robot, which belongs to the technical field of intelligent robots, and comprises the following steps that: according to the binocular baseline length and the focus, the ground parallax values of all rows in an image are analyzed through the geometrical configuration of the known image; based on the ground parallax values, a back projection model calculates the three-dimensional coordinates of a scene point corresponding to a pixel so as to initially judge whether the pixel belongs to an obstacle or a ground point; the obstacle and the ground point are respectively endowed with different colors; the results are post-processed and the false obstacle is removed; and three-dimensional errors are removed and a grid map is established. The method is applicable to various complicated indoor environments, can precisely identify various obstacles, has very high real-time performance, and provides very good preparation conditions for the robot to avoid obstacles.

The invention discloses a depth image estimating method of a binocular stereo video, which comprises the following steps of: (1) collecting a binocular stereo video image from a binocular stereo camera; (2) performing parallax estimation based on graph cut stereo matching on the binocular stereo video image; (3) performing uniformity check on a parallax image obtained by the graph cut stereo matching, and deleting the unreliable matching by self-adapting matching to reduce the wrong matching of the depth image; (4) converting the parallax image into a depth image according to the relation of the parallax and the depth; (5) performing correction optimization on the obtained depth image by a multilateral filter; and (6) outputting the depth image to finish the depth image estimation of the binocular stereo video. The method disclosed by the invention has the advantages of effectively eliminating the errors in the depth image estimation, and finally obtaining an accurate and dense depth image so as to satisfy the demand of rebuilding image quality based on a real scene.

An autonomous obstacle-avoiding planning method of a tour detector based on a binocular stereo vision is characterized by: generating DEM data of a local topography; constructing a raster map of a field range of a binocular stereo vision system, carrying out DEM data statistics to each grid in the map and carrying out de-noising processing to the DEM; carrying out topography traversability analysis so as to generate a suitability map; using the suitability maps generated during back and forth two planning to carry out data fusion and obtaining the fused suitability map which is used in the obstacle-avoiding planning; placing several alternative path arcs on the suitability map; evaluating the each alternative path arc from two aspects of a distance from the alternative path arc to a target point and a safe obstacle-avoiding ability; selecting the alternative path arcs whose evaluation values are higher a setting threshold from all the alternative path arcs and selecting the path arc who has the highest evaluation value as a result of the obstacle-avoiding planning. The method of the invention is simple and complete. Security is high. Planning efficiency is high. Engineering realization is easy to be achieved. The method can be used to perform a tour detection task of a complex terrain.

The invention discloses a simple and feasible calibration method for the relative attitude of a binocular stereo camera and an inertial measurement unit on the basis of the absolute gravity direction. The method comprises the steps that a gravity direction vector serves as a reference vector, expressions of the gravity direction vector under a camera coordinate system and under an IMU coordinate system are solved respectively to obtain the expressions of the same vector under the two different coordinate systems, and then the attitude change relationship between the two coordinate systems can be solved; finally, precision verification is performed through reprojection errors, and application of the calibration method in a visual odometer is supplied. Experiments show that the relative attitude between the camera and the IMU can be accurately solved through the method. The method can be used for assisting in vision positioning of mobile robots and spherical robots.

The invention discloses a vehicle detection system based on binocular stereo vision and a method thereof. The system comprises binocular parallel cameras, a DSP processor, communication and other modules. The system is vertically installed above a lane in a top-down mode so as to carry out real-time detection on road vehicles. The method comprises the following steps of (1) carrying out main point difference calibration on left and right cameras in advance and acquiring an accurate main point difference parameter; (2) collecting left and right images in real time so as to carry out detection and extraction of a foreground moving object; (3) carrying out stereo matching on an extracted and acquired foreground object area and acquiring a current frame parallax image; (4) carrying out subsequent processing on the acquired parallax image and removing a false detection area, simultaneously acquiring a two-dimensional ground plane mapping image; (5) through calculation, acquiring correlation parameters of a speed, a vehicle height, a vehicle model and the like. In the invention, the binocular cameras are used; a stereo vision principle is used so as to acquire depth information of the object; problems that a monocular vision technology is sensitive to light changes and is easy to be disturbed by shadow are solved.

The invention discloses a binocular stereo matching method based on a convolutional neural network. For matching cost calculation, context information is integrated by using dense blocks on the basisof initial features. For matching cost aggregation, a regularization cost amount of a small coding and decoding structure is provided. And for parallax calculation, a differentiable soft argmin operation is executed on the parallax dimension of the cost quantity to obtain an initial parallax. For parallax refinement, the initial parallax is guided to be refined by taking a residual block as a mainpart and taking similarity measurement as an auxiliary part. According to the method, four stages of a stereo matching algorithm are strictly followed, and four steps are integrated into one network,so that the network can be trained end to end. According to the stereo matching method, contextual information is integrated in the feature extraction process, mismatching of pixel points in an ill-conditioned area is effectively relieved, memory occupation and running time in the network training / speculation period are remarkably reduced through small and medium coding and decoding structures inthe regularization process, and parallax prediction precision is improved.

The invention provides a three-dimensional reconstruction method for binocular stereo vision. The method comprises the steps of performing image pair collection: obtaining images of a calibration plate and an object; performing camera calibration: performing calibration on a camera based on a Zhengyou Zhang calibration method to obtain initial internal and external parameters of the camera; performing stereo rectification: performing rectification on a distortion parameter through epipolar constraint to obtain a rectified image pair; performing image processing: obtaining a region of interest,and balancing brightness difference between left and right images, thereby searching for matching points; performing stereo matching: implementing a multi-grid intensive stereo matching algorithm toobtain a continuous smooth disparity map; and performing point cloud rebuilding and model rebuilding: obtaining a point cloud chart of the object, performing point cloud optimization, and obtaining acomplete object model through triangulation of a greedy algorithm. The smooth model with no obvious difference with the real object can be obtained; and the method can be used for three-dimensional reconstruction of multiple scenes.

A method for measuring space-circle geometric parameter based on binocular stereo-vision includes confirming parameter and one-order radial distortion parameter separately in left-right video cameras, calculating out rotary matrix and translation vector presenting relative position relation of left-right video cameras, utilizing left-right video cameras to obtain two frame of images containing space-circle oval image and carrying out distortion calibration on two obtained images and directly calculating out all geometric parameters of space-circle in linear way by adapting oval image of distortion-calibrated image plane.

The invention relates to a multipoint touch method based on binocular stereoscopic vision, which relates to the technical field of computer vision. The method comprises the following steps: firstly, the azimuth and the projection parameters of a camera relative to a three-dimensional scene are determined through camera calibration, and a binocular camera is utilized to acquire an image pair; secondly, two pictures on the same frame which are acquired on the first step are taken as input, and a scene depth map is calculated by the stereoscopic vision algorithm; thirdly, closest patches are obtained through Gaussian blur and determined thresholds, and central coordinates of the patches are calculated; and fourthly, a plurality of coordinates obtained in the third step are taken as input of a multipoint touch program, and interactive results are returned to a screen. The multipoint touch method based on binocular stereoscopic vision can realize completing the multipoint touch operation in space by a user without touching the screen, and coordinate information of input points acquired is three-dimensional, so that the multipoint touch method can realize more complex interaction such as push and pull of a virtual object and so on.

The invention relates to a vision-based autonomous unmanned plane landing guidance device and method. The method comprises the following steps: carrying out real-time detection on a strong light identification lamp which is carried right ahead an unmanned plane after entering an autonomous landing guidance runway by utilizing measurement cameras which are subjected to off-line calibration and are arranged on two sides of the runway, and acquiring three-dimensional space position information of the unmanned plane via a binocular stereo vision measurement technology by utilizing the four cameras which are accurately calibrated within a large-scale scene range so as to trace and position the unmanned plane to obtain the position, the speed and other flight parameters of the unmanned plane in real time; and transmitting the flight parameters into a flight control system by virtue of a wireless data transmission chain and regulating the flight parameters via the flight control system according to a current status of the unmanned plane to guarantee that the unmanned plane is in stable flight, thereby accurately realizing the autonomous landing of the unmanned plane.

The invention provides a fundus OCT imaging method utilizing three-dimensional imaging of binocular stereo vision and a system thereof with the purpose of solving the problem that three-dimensional correction of phase positions, spatial positions and angles of OCT signals is not precise. The method comprises following steps of: synchronously collecting a binocular stereo vision imaging system and a fundus OCT system; utilizing a self-calibration result of the binocular stereo vision imaging system and acquiring a rotation matrix ROI and a translation vector TOI between an OCT coordinate system and an coordinate system for binocular stereo vision and re-establishing three-dimensional coordinates of bifurcation points of retina surface vessels; acquiring a rotation matrix RIE and a translation vector TIE from the coordinate system for binocular stereo vision to a coordinate system of human eyes; extracting a retina B scanning image; utilizing the rotation matrix ROI and the translation vector TOI in order to achieve conversion of the B scanning image from the OCT coordinate system to the coordinate system for binocular stereo vision; utilizing the rotation matrix RIE and the translation vector TIE in order to realize conversion of the B scanning image from the coordinate system for binocular stereo vision to the coordinate system of human eyes; obtaining a three-dimensional OCT image with uniform spatial distribution by interpolation of the converted B scanning image in the coordinate system of human eyes.

The invention discloses a self-adaptive weighted stereo matching algorithm, comprising the steps of: obtaining a binocular stereo image; selecting a pixel supporting window, calculating the real supporting window of each pixel, wherein the supporting windows formed from the supported pixels have different sizes and shapes; calculating the supporting weight in the supporting window; calculating collected energy cost functions; calculating the parallax value of the pixels; and obtaining an original left-to-right parallax map. The invention also discloses a stereo display and collecting device and a system. The self-adaptive weighted stereo matching algorithm, the stereo display and collecting device and the system can be applied to the technical field of stereo display to improve the effect on stereo matching, as well as obtaining necessary three-dimensional information through less data volume.

The invention relates to a method for automatically determining relative positions and postures of a binocular stereo measuring system in measurement at different visual angles. The method comprises the following steps of: when a binocular stereo measuring system is utilized for measuring at each visual angle, remaining the internal constraint condition that the relative positions and postures of a left camera and a right camera are known constant, combining matched point pairs respectively generated by the left camera and the right camera in different measurement processes, constructing a multi-visual angle geometric constraint relation and automatically resolving initial values of relative positions and postures of the stereo sensor in twice measurement; then reconstructing three-dimensional feature points and a constraint relation among image points in all images and the relative positions and postures of the stereo sensor by multi-visual angle measurement, and optimizing and determining the relative positions and postures at all measurement visual angles. According to the method, the self positioning of the stereo sensor and the accuracy and the reliability of data combination are improved and the combinable conditions of multi-visual angle measured data are reduced.

The invention discloses a distance measurement method and system based on binocular stereo vision. The method comprises the following steps: acquiring a position relationship between two fixed points; constructing a distance measurement model by using one fixed point as the original point according to the position relationship; and acquiring unknown parameters through easy calibration according to the model so as to realize the distance measurement of a target. The method can enlarge the information amount of infrared-visible images and provide position information of the target by using a heterogeneous source consisting of a thermal imager and a visible CCD (charge coupled device) camera to measure the distance.

A pruning robot system for grape vines comprises an automatic navigation module, a pruning module and a binocular stereo vision module. The automatic navigation module comprises a tracked truck, a pan-and-tilt camera, a left wheel driver, a right wheel driver, and an industrial personal computer. The pruning module comprises an agricultural mechanical arm and terminal pruning shears, wherein the agricultural mechanical arm is used for mounting the terminal pruning shears and guiding the same to shear branches. The terminal pruning shears are used for pruning branches. The industrial personal computer is used for analyzing and processing image information of the grape vines, extracting coordinate information of pruning points, planning trajectory of the mechanical arm and giving out movement directives to joints. The binocular stereo vision module comprises two industrial cameras, an image acquisition card and the industrial personal computer, wherein the two industrial cameras are used for acquiring image information of the grape vines, and the information is transmitted to the industrial personal computer for image identification and processing through the image acquisition card. By the aid of the pruning robot system, labor intensity can be lowered, and work efficiency can be improved.