681 results about "3d camera" patented technology

The invention provides a linear dimension measurement method and device for the total length of a road guardrail stand column, and relates to the field of linear dimension measurement, the device is deployed on a detection vehicle, and comprises a control module, a ground penetrating radar measurement module, a 3D camera measurement module, a sensor module, a lower computer and an upper computer; according to the invention, accurate data acquisition is realized through multi-module cooperative control, and the lower computer is combined with the AI image recognition technology to realize accurate acquisition of contour data; the lower computer transmits radar original data (reflected waves and diffracted waves) and 3D contour data (column cap space coordinates) to the upper computer, the upper computer achieves data fusion through a hierarchical velocity model (time-depth conversion) and a coordinate system mapping algorithm (3D-road coordinate system), and the accuracy of the total length measurement error is greatly improved compared with that of single module detection. The method solves the problems of low efficiency, large destructiveness and poor accuracy of traditional manual detection, and has the advantages of method innovation and engineering landing.

The invention discloses a visual angle tracking grating stereo display system comprising a 3D camera grating display device used for obtaining viewer 3D information; the eyeball position tracking module is used for acquiring real-time spatial position coordinates of the eyes of the audience relative to the 3D camera; the pose calibration module is used for acquiring a spatial relationship between the 3D camera and the display; and the visual angle rendering and grating interlaced graph generation module is used for calculating the spatial position of the human eyes on the display screen and the mapping of each grating strip in a screen pixel coordinate system after acquiring the positions of the eyes and the relative poses of the display screen and the 3D camera, generating a grating stereoscopic display image suitable for the eyes, and sending the grating stereoscopic display image to a display to be output. According to the method and the device, the viewpoint self-adaptive three-dimensional display of the eyes of one or more audiences is realized, the calculation complexity is reduced, the three-dimensional vision consistency can be ensured, the picture which should be seen by the viewing angle can be accurately rendered, and the problem of blurring or 3D picture distortion is avoided.

The invention belongs to the technical field of machine vision fusion application, and particularly relates to a composite robot CNC feeding and discharging method and device. Comprising a composite robot and a CNC machine tool; the composite robot comprises a movable chassis, a mechanical arm, a feeding and discharging clamping jaw, a 3D camera and a 2D camera. The 3D camera and the 2D camera are mounted on a mounting platform at the tail end of the mechanical arm; the 2D camera and the 3D camera are added in the first deployment stage and the nth deployment stage to assist deployment, teaching of each CNC machine tool is not needed, the 3D camera is dismounted in the feeding and discharging stage, only the 2D camera is used in the feeding and discharging stage, and the cost is lower; and meanwhile, a 2D positioning identification plate or a 3D positioning identification plate is adopted, the feeding base does not need to be photographed, and the influence of cutting liquid, metal filings, discharging workpieces and the like on the camera algorithm precision is avoided.

In accordance with one aspect of the inventive concepts, provided is a method and a system for horizontal infrastructure localization, comprising a robotic vehicle platform, such as an AMR, a mechanism for collecting sensor data, e.g., point cloud data, such as a LiDAR scanner or 3D camera, and a processor configured to process the sensor data to identify and determine a position and orientation of the horizontal infrastructure. The robotic vehicle processes the sensor data to identify a horizontal infrastructure, determine its pose, and then determine if an area of a horizontal surface of the horizontal infrastructure is clear for dropping a load, e.g., palletized load.

The invention is suitable for the field of image processing, and discloses a fast fusion method for point cloud data of multiple 3D cameras, terminal equipment and a storage medium. The multi-3D-camera point cloud data fast fusion method comprises the steps that calibration data of a plurality of 3D cameras are collected, the calibration data comprise an RGB image, a depth image and an internal reference matrix, and a calibration plate needs to appear in the view fields of the 3D cameras at the same time; generating a three-dimensional point cloud coordinate point set according to the calibration data, and calculating a transformation matrix between the reference camera coordinate system and the non-reference camera coordinate system according to the three-dimensional point cloud coordinate point set; and according to the transformation matrix, registering the three-dimensional point cloud coordinate point set to a preset reference camera coordinate system to obtain merged target point cloud data, the target point cloud data including RGB color values. According to the invention, multi-view complementation is realized, a physical blind area of a single camera can be covered, and the problem of point cloud missing caused by insufficient view field and shielding is solved.

The invention discloses an assemblage multi-robot collaborative welding system in ship sections, and relates to the technical field of industrial robot collaborative control, the assemblage multi-robot collaborative welding system comprises an industrial internet platform, the industrial internet platform is in communication connection with the following modules: a visual inspection modeling module, which is used for scanning the surface of a workpiece through a 3D camera, constructing a point cloud model, and establishing a point cloud model; matching the point cloud model with a design drawing, and identifying a deviation between an actual workpiece and the drawing; and the path planning and correcting module is used for planning a collision-free welding path in combination with the visual detection data and the motion range of the robot. Through high-precision visual inspection scanning and an SLAM matching algorithm, a workpiece point cloud model is constructed in real time and is accurately aligned with a design drawing, the welding seam position and the workpiece geometric deviation can be accurately recognized, a collision-free welding path is generated in combination with robot kinematics constraint and a safety boundary, it is ensured that the pose of the tail end of a welding gun is attached to the surface of the workpiece, and the welding quality is improved. Welding deviation is reduced, and process stability is ensured.

The invention relates to the technical field of industrial vehicle intelligence, in particular to an intelligent flexible forklift system based on multi-mode sensing and self-adaptive path planning. The intelligent forklift comprises an intelligent forklift body, a vehicle-mounted integrated control system, a multi-mode sensing and navigation module, a self-adaptive decision-making and path planning module, a cooperative communication and task management module and a cloud scheduling and management platform, and the intelligent forklift body is provided with a pallet fork mechanism with a 3D camera and a force sensor; the vehicle-mounted integrated control system serves as a core to achieve multi-module data interaction and instruction output, the multi-mode sensing and navigation module generates an environment situation map through multi-source data fusion, the self-adaptive decision-making and path planning module completes path planning, obstacle avoidance and pre-verification, the cooperative communication and task management module achieves multi-vehicle cooperation and task disassembly, and the multi-mode sensing and navigation module generates an environment situation map through multi-source data fusion. And the cloud scheduling and management platform realizes global monitoring and management. According to the method, the adaptability to complex scenes and the operation safety are improved, and accurate forking and flexible path planning are realized.

The invention relates to the technical field of welding, and discloses a teaching-free angle steel welding method which comprises the following steps that S1, a robot carries a 3D camera to execute self-adaptive high-angle shooting exploration, and workpiece point cloud data are obtained until recognition succeeds or exceeds a preset exploration range; s2, dividing the ROI into grids according to the view field of the camera by taking the distance of the point cloud detected for the first time as a reference; s3, performing expansion type multi-angle photographing from the initial grid to the periphery, stopping scanning when meeting an empty grid, and obtaining an effective area point cloud of the workpiece; s4, performing regional progressive registration on the point clouds of the adjacent shooting regions, and eliminating registration drift through closed-loop detection and pose image optimization; and S5, calculating the normal and curvature of the point cloud. According to the intelligent angle steel welding method, the robot carries the 3D camera to execute self-adaptive high-angle shooting exploration to obtain the point cloud data of the workpiece, and therefore the problems that due to the fact that manual teaching or off-line programming is mostly adopted in a traditional intelligent angle steel welding method, the welding efficiency is low, complex workpiece path planning consumes long time, and precision fluctuation is large are solved.

A ship sub-assembly weld seam identification method, comprising: creating a point cloud weld seam identification training dataset by means of point cloud discretization, virtual sampling, weld-zone semi-automatic interactive annotation, and style transfer-based augmentation of ship sub-assembly CAD data; training a point cloud weld seam identification model, and exporting a weld seam identification model data file; using a 3D point cloud camera array as a sampling camera to collect workpiece 3D point cloud data of a sub-assembly workpiece to be welded at the production site; by means of the weld seam identification model data file, obtaining weld seam annotation position data and positioning a weld seam; and issuing a welding process plan and instruction to control a welding robotic arm and a control device therefor to complete sub-assembly workpiece welding. Also provided are a ship sub-assembly weld seam identification system, a computer device, and a computer-readable storage medium. The method overcomes the shortcomings in conventional 3D weld seam identification methods such as point cloud incompleteness and identification failures caused by steel types and the imaging angles of 3D cameras, thereby greatly improving the adaptability of automatic weld seam identification methods to complex workpieces.

The invention discloses a large-size complex surface type high-speed scanning measurement device based on a white light interference technology and a method thereof, and relates to the technical field of crossing of optical precision measurement and machine vision, and the device comprises an intelligent measurement module which integrates a large-view-field vision camera, a 3D camera and a white light interferometer, the large-view-field vision camera is used for acquiring a global two-dimensional image of a measured object and automatically identifying features to trigger a measurement program, and the 3D camera is used for acquiring three-dimensional point cloud data of the measured object; according to the invention, the intelligent measurement module integrates multiple devices, a large-view-field camera triggering program, point cloud acquisition by the 3D camera and high-precision measurement by the white light interferometer, so that multi-source data collaboration is realized. The dynamic scanning optimization module adjusts step lengths and grid priorities according to curvature regions, improves an algorithm to generate a path, improves scanning efficiency and pertinence, achieves combination of global and local measurement through cooperation of multiple devices, meets the multi-precision requirements of complex surface types, improves measurement efficiency, and guarantees the precision of key regions.

The invention relates to a railway track abrasion detection method and system, and aims to realize accurate and efficient detection of turnout sharp point rail abrasion. A detection device carrying a 3D camera, a speed sensor, a signal processing module and a data processing terminal is arranged in a turnout area to be detected, parameter configuration is completed, in the constant-speed moving process, the speed sensor generates a trigger signal according to the specified distance, the 3D camera is driven to synchronously collect point cloud data of the two-dimensional contour of the cross section of the sharp point rail, and a three-dimensional data stream is formed; after data are denoised, section registration splicing and three-dimensional model reconstruction are completed in combination with distance stamps, an actual measurement model and a standard contour model are superposed and compared, abrasion parameters are calculated, and the maximum abrasion point and position are recognized; and finally presenting the abrasion data in the forms of a color cloud picture, a table and the like, and automatically generating a standard detection report containing the contents of line information, defect types and the like. According to the method, automation and visualization of abrasion detection are achieved, the detection precision is high, the efficiency is high, and reliable data support is provided for track maintenance.

The invention belongs to the technical field of unmanned aerial vehicle battery replacement, and provides an unmanned aerial vehicle automatic battery replacement method and system based on 3D visual guidance, and the method comprises the steps: constructing a target feature template of each region of a battery compartment and a feed battery based on obtained standard image data; performing multi-stage pyramid matching on the obtained to-be-matched point cloud data and the constructed target feature template, and reconstructing a point cloud data set and standard image data subjected to multi-stage pyramid matching; calculating the pose deviation value of the feed battery, and combining the pose deviation value of the feed battery to obtain the actual position of the feed battery; the target position and posture information are updated according to the feed battery posture deviation value, a mechanical arm is controlled to move to the target position according to the updated posture information, and the feed battery is grabbed and placed at the designated position; and controlling the mechanical arm to grab the fully-stored battery and then return to the specified image data acquisition position, controlling the 3D camera to carry out secondary scanning, repeating the steps, and putting the fully-stored battery into the battery compartment of the unmanned aerial vehicle to complete automatic battery replacement operation.

The embodiment of the invention relates to the technical field of automatic unstacking robots, and discloses an unstacking method and system, a controller and a computer readable storage medium, and the method comprises the steps: obtaining RGB image information and point cloud information of an ROI region through a 3D camera, recognizing a to-be-unstacked article in the RGB image information, and generating a grabbing point; the camera coordinates of the grabbing points, the length and width information of the box body of the top layer to be unstacked and the height information of the top surface of the box body are obtained through the point cloud information; and the grabbing step is executed, after the current grabbed box leaves the stack, the height difference of the original position of the current box is obtained through the 3D camera again, the detection height value of the current box is obtained, laser radar verification is selectively adopted according to different conditions, and finally efficient unstacking is achieved. By means of the technical scheme, the problem that in the unstacking process in the prior art, the detection efficiency and the operation precision are difficult to consider at the same time is solved.

The invention relates to a tire character recognition method, in particular to a tire character recognition method based on three-dimensional point cloud, and the method comprises the steps: calibrating an industrial 3D camera, managing and controlling the connection and communication of the camera through a tire point cloud data acquisition module, and visualizing and storing an acquisition result; performing combined filtering on the tire point cloud data by using voxel downsampling and a statistical filtering method, and then making a tire data set; putting the manufactured data set into a PointNet + + network added with a self-attention mechanism for training, and generating a training weight; and visualizing a tire identification result and storing a prediction result file. The invention relates to the technical field of three-dimensional recognition, geometric characteristics of the surface of a tire are more accurately captured by using point cloud, the influence of factors such as light and tread stains is reduced, hierarchical feature extraction of PointNet + + is combined with a Transform self-attention mechanism, detail features are more sufficiently extracted by using the relationship between different hierarchical features, and the recognition accuracy is improved. And the robustness of tire character recognition in a complex environment is improved.

The invention provides a cement conveyor volume measurement method based on deep learning point cloud segmentation. The method comprises the following steps: obtaining 3D point cloud data by using a laser line scanning 3D camera, and determining RGB values of pixels corresponding to the 3D point cloud data to obtain 3D point cloud with color features; inputting the 3D point cloud with the color features into an improved Point-Net + + neural network for 3D point cloud segmentation, and obtaining 3D point cloud data only containing the cement surface in the conveyor after segmentation; constructing a triangulation network based on the cement irregular surface in the segmented 3D point cloud data by adopting a triangulation method, and performing volume calculation according to the constructed triangulation network; wherein the improved Point-Net + + neural network is constructed on the basis of a framework of an original Point-Net + + point cloud segmentation network, and comprises a core point convolution, a context sensing module and a Focal Loss loss function. Therefore, the performance of a point cloud segmentation algorithm is improved, and even under complex conditions, the surface point cloud data of the cement material in the conveyor can still be accurately segmented for volume measurement.

A bearing defect detection system and method based on multi-combination lightsource and multi-view multi-channel YOLO network structure detection algorithm, in which the system includes a rotating platform, a multi-combination lightsource, a light source controller, a 2D camera and lens, an industrial control machine processing unit; The rotating mechanism is used for the rotation of the bearing at any 360 degrees, the light source adopts a set of adjustable coaxial or ring light sources, the camera is all 2D cameras, for pits, scratches, burrs and other three-dimensional defects, improved by multi-view multi-channel YOLO network structure detection algorithm, replacing the current expensive 3D camera detection methods, improve detection accuracy, reduce system costs.

The invention discloses a method and a system for generating a long video with a controllable camera track based on a video diffusion model. Comprising a camera track and initial frame preparation stage, a point cloud construction and multi-view image generation stage, a scale factor alignment optimization stage, a camera motion prior injection and noise initialization stage, a diffusion inversion generation stage and a sliding window time consistency fusion stage. Three-dimensional camera track modeling, projection reconstruction and diffusion processes are combined, a video content generation process is explicitly guided to be aligned with a track path set by a user, and long video generation which is reasonable in structure, natural in vision and continuous in time is achieved.

The invention provides a welding seam quality detection method and device based on multi-modal data, equipment and a medium. The method comprises the steps that target point cloud data and target temperature data for a target welding area are obtained; fusing the target point cloud data and the target temperature data to obtain target infrared point cloud data; and according to the target infrared point cloud data, generating a welding seam detection result aiming at the welding seam quality in the target welding area. According to the invention, the problem of insufficient weld data extraction caused by single three-dimensional image point cloud data acquired by the 3D camera and easy influence by the surrounding environment in a laser scanning detection method can be solved; and the problems of poor detection precision and low universality which are finally caused by large welding seam extraction difficulty and large deviation of an extraction result in a visual detection algorithm can also be solved. In addition, the automatic detection mode can solve the problems that in a traditional method, the real-time performance is poor and the efficiency is low due to the fact that the appearance quality of the weld joint needs to be manually detected in an off-line mode.

The invention discloses a method for detecting the head height of a bolt, and the method comprises the following steps: carrying out the projection of the head of the bolt through employing a structured light 3D camera, and obtaining the three-dimensional point cloud data of the head of the bolt; after abnormal points are processed, a bolt head area template is constructed; bolt head positioning is carried out on the bolt head area template and the actual detection area; selecting a plurality of local point cloud data in the bolt head area, and setting the local point cloud data as bolt head positions; selecting a plurality of local point cloud data on the outer side of the bolt head area, and setting the local point cloud data as bolt reference surface positions; and calculating the distance between the bolt head position and the bolt reference surface position by using a point-to-plane algorithm. The bolt head height detection method provided by the invention has the advantages of one-key operation, simple operation, fast measurement speed, automatic data and picture storage, and solving of pain points which cannot be traced by conventional contact measurement.

Systems and methods for performing surface matching using three-dimensional (3D) and color information. An example method includes obtaining, by a 3D camera, a first 3D image of a field of view. The 3D image includes (i) a plurality of voxels, or 3D points, of the field of view and (ii) color information of the field of view. A processor normalizes the color information into a common color space, and determines scene histograms from the normalized color information. Each of the scene histograms is determined for a voxel of the plurality of voxels. The method further includes determining a color score from at least the scene histograms and model histograms, the model histograms being indicative of color information of a model image of a 3D object, and determining the presence of a surface of an object in the first 3D image from the color score.

The invention discloses a track non-contact intelligent detection system and method based on multi-source data fusion, relates to the technical field of track detection, and aims at efficiently completing track detection with low cost. The system comprises a frame body, a plurality of line structured light 3D cameras, a synchronous controller, a processing computer and a speedometer; the linear structured light 3D cameras are arranged on the two sides of the frame body, and the optical axes of the linear structured light 3D cameras are arranged to be perpendicular to the axis direction of the steel rail to be measured; the synchronous controller, the processing computer and the speedometer are integrated at the bottom of the frame body, and the synchronous controller and the speedometer are respectively connected with the structured light 3D camera through cables.

The invention relates to the technical field of automation, in particular to a real-time multi-target visual positioning and mechanical arm cooperation system for intelligent material sorting, which comprises a visual perception and information acquisition module, a data processing and path planning module, a mechanical arm execution module and a man-machine interaction module. By combining the 2D camera and the 3D camera, 360-degree full-view-angle material sensing can be achieved, and the possibility of repeated grabbing errors of the mechanical arm can be reduced through the design of judging whether grabbing succeeds or not after the mechanical arm is controlled to execute the grabbing step every time.

The invention relates to the technical field of pipeline production, and aims to solve the technical problems of leak detection, high labor intensity of manual repair and the like. In order to solve the technical problem, the invention provides the pipeline robot and the method for detecting and repairing the defects of the inner wall of the pipeline. The pipeline robot comprises a robot body walking in a pipeline; the three-dimensional detection assembly is arranged at the front end of the robot body; a laser of the three-dimensional detection assembly emits a laser beam and forms a light ring on the inner wall of the to-be-detected pipeline; the 3D camera is used for shooting light rings; the lifting assembly is connected with the robot body and the three-dimensional detection assembly to drive the three-dimensional detection assembly to lift; the repairing assembly is arranged at the tail end of the robot body. A polishing brush of the repairing assembly is connected with a rotary power part so as to drive the polishing brush to rotate in the horizontal plane and the vertical plane. According to the invention, surface defects can be detected with high precision and high efficiency, detection and repair are integrated, and labor intensity is reduced.

The utility model discloses a multifunctional mobile platform for detecting, interfering, trapping, striking and picking up an unmanned aerial vehicle. The multifunctional mobile platform comprises a mobile platform and portable reconnaissance and striking equipment which is arranged on the mobile platform and is used for striking the unmanned aerial vehicle, the mobile platform is provided with a positioning navigation antenna for autonomous navigation; the mobile platform is provided with a navigation laser radar for ground environment detection. An unmanned aerial vehicle countering antenna and an unmanned aerial vehicle countering controller are arranged on the mobile platform, and the unmanned aerial vehicle countering controller is in communication connection with the unmanned aerial vehicle countering antenna; a pickup unit is arranged on the moving platform, the pickup unit comprises a mechanical arm mounted on the moving platform, a pickup clamping jaw assembled at the end of the mechanical arm and a 3D camera, and the pickup clamping jaw, the 3D camera and the mechanical arm are in communication connection with the unmanned aerial vehicle countering controller. According to the utility model, the unmanned aerial vehicle countering controller sends a control instruction to the 3D camera and carries out visual search on the forced landing unmanned aerial vehicle; and a control instruction is sent to the mechanical arm and the picking clamping jaw to pick the unmanned aerial vehicle.

A controlling system of an endoscopic surgical robot based on head tracking is disclosed. The system includes a host, the robot and a complex camera. The robot holds an endoscope at a specific position through a specific gesture. The complex camera includes a 2D camera for obtaining facial features of a doctor to identify whether the doctor is a registered user. The complex camera includes a 3D camera for obtaining depth information of the doctor's head. The host estimates a 2D space information and a 3D space information of the doctor's head according to the facial features and the depth information, and transforms the 2D space information and 3D space information to a robot moving information by a transformation matrix to control the robot to move.

The invention particularly discloses a fractured rock mass grouting three-dimensional visualization test device and method, and relates to the technical field of rock mass engineering. The device comprises a test box body, a liquid inlet and outlet system, a monitoring system and a dynamic water environment simulation assembly, the test box body is made of a transparent acrylic material and is composed of an upper cover plate, a lower cover plate, a front cover plate, a rear cover plate, a baffle and two side cover plates, and a core experiment module is arranged in the test box body; the liquid inlet and outlet system is sequentially connected with the liquid holding device, the peristaltic pump, the valve, the Doppler flow velocity sensor and the test box body through rubber pipes; the monitoring system integrates an image collection unit and a data monitoring unit, an industrial-grade 3D camera and a binocular camera achieve visualization of the displacement process, and a fiber bragg grating pressure sensor synchronously collects fracture pressure data and transmits the fracture pressure data to a computer through a multifunctional data collection module. The device can be used for observing the displacement process of the slurry in the three-dimensional fracture in real time, synchronously monitoring key parameters, adapting to complex test working conditions and providing reliable support for grouting law research.

The invention relates to the technical field of robot vision, in particular to a three-dimensional reconstruction viewpoint planning method based on collaborative robot 3D vision, which comprises the following steps: S1, performing visual calibration on a 3D camera on a collaborative robot; s2, whether a workpiece standard model exists or not is judged, if yes, a candidate viewpoint set is constructed according to the workpiece standard model, and if not, the workpiece standard model is established according to preset fixed multiple viewpoints, and then the candidate viewpoint set is constructed; and S3, a deep reinforcement learning mechanism obtains a planning result according to the candidate viewpoint set, controls an actual six-axis collaborative robot to move according to the planning result, carries out data acquisition, obtains 3D point cloud data of the workpiece at the visual angle, carries out preprocessing including noise point removal and filtering, and generates a three-dimensional model of the workpiece based on the fused point cloud data. The three-dimensional reconstruction method is high in automation degree, almost does not need manual operation in the whole process, and can effectively perform efficient and rapid three-dimensional reconstruction on a large batch of different types of non-standard customized small-batch workpieces.

The invention relates to a feeding table position registration method based on 3D vision, belongs to the technical field of position registration, and solves the problem of low precision of positioning compensation of a feeding table in the prior art. The method comprises the steps that at least three circular marks are arranged on a workpiece; after the feeding table is controlled to convey the workpiece to a designated position, an image of the workpiece at the current position is collected through the 3D camera to serve as a target image; acquiring a workpiece point cloud from the target image as a target point cloud based on ellipse fitting processing on the circular mark; matching the target point cloud with the reference point cloud to obtain translation deviation and rotation deviation between the current position and the specified position; the reference point cloud is a workpiece point cloud of a specified position collected under a template attitude; and based on the translation deviation and the rotation deviation, the feeding table is controlled to move to a designated position. By means of the method, the precision of positioning compensation of the feeding table can be effectively improved.

The invention relates to the technical field of automatic production, in particular to an automatic unstacking method for air conditioner condenser cores. According to the technical scheme, the method comprises the following steps that a 3D camera is arranged above a condenser core feeding platform, and the side faces of cooling fins of a condenser core are recognized; a robot is used for driving a clamp to clamp and move the uppermost condenser core to a discharging conveying line according to the two fin side faces, recognized by the 3D camera, of the condenser core; a 2D camera is arranged on the discharging conveying line to recognize and detect the end type of the condenser core, and finally the robot obliquely places the condenser core on the discharging conveying line in the direction that the U-shaped pipe end faces downwards according to the end type of the condenser core. The method has the beneficial effects that the recognition efficiency can be improved on the premise of ensuring the recognition accuracy; and in addition, a 3D recognition method and a 2D recognition method are optimized, the recognition efficiency is further improved, and the automatic unstacking effect is improved.

The present invention discloses a method for measuring the lead of steel bars based on 3D vision. First, a depth map of the steel bars is captured by a 3D camera, and then the threshold segmentation algorithm is used to extract the rectangular contour of the steel bars and extract the center line of the rectangle. Next, the depth values of all points on the center line are obtained and exported to an Excel table. Subsequently, based on the exported Excel table, Python is used to draw a function graph of the pixel coordinates and depth values of the points on the center line. Then, the filtering algorithm is used to optimize the function graph and find the maximum points. Finally, two maximum points between the specified pixel coordinate points are selected to calculate the lead of the steel bars. The present invention realizes the non-contact measurement function of the lead of steel bars with automation, and has a relatively fast measurement speed and good measurement effect for the lead of steel bars. It also avoids the large lead measurement error caused by the measurement method based on 2D vision. This measurement method has high measurement accuracy and is less affected by the environment, and is suitable for being widely promoted and used.