893 results about "Binocular single vision" patented technology

The invention discloses a mechanical arm natural language instruction control system and method based on a large language model, and belongs to the field of intelligent manufacturing. Aiming at the limitation that traditional mechanical arm control depends on pre-programming and a static rule library, a dynamic mapping mode from a natural language instruction to an atomic action sequence is designed, an atomic skill library including detection, grabbing, moving, placement and other operations is constructed, and semantic analysis and a multi-mode cooperation technology are combined, so that the atomic action sequence is obtained. And support is provided for man-machine cooperation of a flexible assembly task. The method specifically comprises the steps that a DeepSeek-Distil-Llam-8B large model and a LoRA fine tuning technology are adopted, and a natural language instruction is converted into an executable atomic action sequence; based on a transfer learning optimized YOLOv8 target detection technology and a binocular vision positioning technology, a sensing module adaptive to an assembly scene is constructed and is fused with a mechanical arm motion planning module, and positioning grabbing of parts and tools is achieved. And an interactive interface is built by combining a voice-to-text large model and a Gradio front-end framework, so that the convenience of man-machine interaction is improved. By optimizing large model reasoning and motion planning cooperation efficiency, response delay from instructions to execution is reduced, and an efficient and extensible solution is provided for man-machine cooperation in intelligent manufacturing.

The invention discloses an inspection robot navigation method, system and device based on Beidou and binocular vision fusion and a storage medium, and relates to the field of robot navigation and obstacle avoidance, and the method comprises the following steps: carrying out multi-modal data fusion processing based on an initial coordinate of an inspection robot and an image captured by a binocular vision system to obtain an environment sensing result; carrying out global planning and local optimization based on an environment perception result, and carrying out cooperation to generate a trajectory planning scheme; based on the environment sensing result and the trajectory planning scheme, intelligent navigation cooperative regulation and control are carried out, and an inspection robot motion control strategy is obtained; by improving the environmental perception accuracy, optimizing the path planning and performing intelligent navigation regulation and control, the navigation problem of the inspection robot in complex environments such as a transformer substation can be effectively solved, the inspection safety and efficiency are improved, and the method has remarkable practical application value.

The invention relates to the technical field of machine vision and perception, in particular to a ton bag lifting unmanned control system based on a binocular vision camera and a laser radar, which comprises an intelligent control unit, a lifting appliance executing mechanism, a sensing unit and a special ton bag, the sensing unit comprises a binocular vision camera and a laser radar and is used for collecting depth vision and three-dimensional point cloud information of an operation area; the intelligent control unit fuses multi-source data, locates a lifting lug by improving a weighted multi-feature fusion algorithm, plans a safety path and generates a staged instruction; the lifting appliance executing mechanism lifts and pulls a collapsed lifting lug through an electromagnetic adsorption module, a mechanical gripper module clamps the lifting lug, and reliable operation is achieved in cooperation with a verification mechanism; the special ton bag is matched with a sensing and executing module through a high-contrast color and a pre-embedded metal piece. The full-process unmanned operation is achieved, the robustness and operation safety of the complex environment are improved, and the ton bag hoisting requirements of multiple industries are met.

The invention discloses an intelligent obstacle avoidance system and method for a low-altitude aircraft. Efficient obstacle avoidance is realized by fusing an improved attention mechanism and a binocular vision sensing multi-dimensional analysis model. In the system, a binocular vision perception unit collects double-path images and establishes motion association, a feature enhancement attention unit performs dynamic weighting according to image features, and a multi-dimensional analysis processing unit constructs a three-dimensional feature space to accurately recognize obstacles. The path planning decision-making unit combines the aircraft state and a safety threshold value, an optimal obstacle avoidance path is screened through heuristic search and cost evaluation, and the motion control instruction generation unit converts the path into a posture and speed control instruction to drive obstacle avoidance. According to the invention, the defects of the traditional technology in obstacle identification and path planning are overcome, dynamic or hidden obstacles can be accurately identified in a complex environment, the obstacle avoidance path is efficiently planned, and the operation safety and reliability of the low-altitude aircraft are improved.

The invention discloses an unmanned aerial vehicle inspection control system based on binocular vision, and relates to the technical field of unmanned aerial vehicle inspection, a binocular vision acquisition module is used for acquiring a to-be-inspected target image, detecting shielding and degree, and extracting a target feature and a distribution rule when an unmanned aerial vehicle inspects according to a preset route; the information acquisition module is used for acquiring a target position, peripheral complementary target distribution, unmanned aerial vehicle pose and kinematics parameters when it is detected that the shielding degree of the target point exceeds a set value. Complete coverage of the two types of target sub-regions can be ensured, the flight trajectory curvature of the second type of target sub-regions can be minimum, and stable flight is ensured.

The invention relates to the crossing field of mechanical engineering and automatic control, in particular to a global path planning and anti-swing control method for a ship unloader, and aims to solve the problems of rigid path planning and poor synergism of out-of-control swing of a lifting appliance in traditional ship unloading operation. The method comprises the following steps: constructing a dynamic three-dimensional operation space model with fusion of a laser radar and binocular vision, and updating obstacle information in real time; an improved fast extended random tree algorithm is adopted to generate a high-smoothness initial path; establishing a six-degree-of-freedom lifting appliance swinging dynamic model and identifying parameters on line; path tracking and anti-swing torque are synchronously optimized through a model prediction controller, and the control period is smaller than or equal to 20 milliseconds; and closed-loop feedback correction is implemented by combining an encoder and an inertial measurement unit. According to the scheme, environment dynamic sensing, path-anti-swing depth cooperation and multi-disturbance self-adaptive compensation are achieved, the operation rhythm is improved by 30% or above, the system still operates stably under the 8-level wind condition, and high precision, high robustness and engineering implementability are achieved.

The invention particularly relates to a binocular vision-based three-dimensional reconstruction method, which comprises the following steps of: calibrating a binocular camera based on an improved Zhang Zhengyou calibration method to obtain internal and external parameters and a distortion coefficient of the camera; performing stereo correction on the image by using the internal and external parameters of the camera and the distortion coefficient obtained by calibration, so that the binocular image meets an epipolar constraint condition; a multi-strategy optimized semi-global stereo matching algorithm is adopted to process the image after stereo correction, and a disparity map is generated; based on the generated disparity map, generating a three-dimensional point cloud through a triangulation principle; carrying out anti-interference processing and registration optimization on the three-dimensional point cloud; and performing global splicing on the three-dimensional point clouds subjected to anti-interference processing and registration optimization based on a sequential registration error sharing strategy to complete three-dimensional reconstruction. According to the method, the key problems of large calibration error, weak texture matching failure, point cloud noise sensitivity and registration accumulative error in a traditional method are solved, and the reconstruction precision and stability are remarkably improved.

The invention provides an intelligent inspection risk assessment method and system based on multi-sensor fusion, and the method comprises the steps: obtaining original multi-source data of a transformer substation, the original multi-source data comprises a binocular vision image, infrared thermal image data and environment sensor data, and carrying out the time-space calibration and preprocessing of the original multi-source data, obtaining a multi-source sensor data stream; carrying out feature extraction on the multi-source sensor data stream to obtain a multi-modal feature set, generating a refined semantic mask based on the multi-modal feature set, constructing an initial scene relation graph, calculating a risk level based on a multi-layer perceptron classifier, and generating a risk level evaluation result and a risk distribution graph; outputting a safety distance violation warning and a risk area identifier; and generating comprehensive risk early warning information based on the risk level assessment result, the risk distribution diagram, the safety distance violation warning and the risk area identifier.

The invention discloses a real-time back break control method of a heading machine based on visual association and digital twinning, which relates to the technical field of intelligent control of coal mining equipment, and comprises the following four steps: processing binocular visual data through a double-domain double-branch Transformer model, extracting coal rock and roadway structure characteristics and outputting cutting head offset; a GCN-LSTM model is adopted to fuse visual, laser radar and fiber-optic gyroscope data, and dynamic calibration and output of a three-dimensional pose are carried out; a roadway three-dimensional model is constructed based on the SLAM technology, and real-time updating of a digital twin model is achieved through edge-cloud collaboration; and in combination with a reinforcement learning strategy, generating a cutting parameter adjustment instruction according to the deviation of the digital twin model, and correcting model parameters through error feedback. The method solves the problems of low visual recognition precision, sensor data drift, digital twinning update lag and the like in an underground complex environment, improves the back break control precision, tunneling efficiency and equipment stability, and is suitable for tunneling operation under complex geological conditions.

The invention relates to the technical field of image data processing and intelligent control, and discloses a patch board spot welding track intelligent control method and system based on visual perception. According to the method, synchronous image streams are collected through a binocular vision sensor, and epipolar correction image pairs are generated through timestamp synchronization, ROI extraction and epipolar correction processing; calculating a disparity map by adopting a stereo matching algorithm, and constructing a compensated three-dimensional point cloud model based on deformation vector field fusion multi-frame point cloud data of a radial basis kernel function; welding spot position error vectors are generated by extracting welding spot feature points and performing spatial filtering optimization; and carrying out inverse kinematics calculation on the mechanical arm by adopting a damping least square method, carrying out safety constraint optimization in combination with prospective collision risk assessment and real-time pose data, and generating a trajectory compensation instruction. According to the method, the problems of dynamic deformation compensation and motion safety in patch plate welding are solved, the submillimeter welding spot positioning precision is achieved, and the welding quality stability and the system robustness are improved.

The invention discloses a robot automatic navigation method and system based on depth vision fusion, and relates to the technical field of intelligent navigation, and the method comprises the steps: carrying out the registration of a point cloud subset of continuous frames through employing an ICP algorithm, distributing weights for a static obstacle and a dynamic obstacle, and carrying out the fusion, and obtaining a fused environment perception data set. The method comprises the following steps: updating a dynamic environment in real time, generating a dynamic environment map, carrying out trajectory tracking on moving targets by using binocular vision equipment, calculating a real-time motion trajectory of each moving target, obtaining an environment hotspot map, generating a global navigation path for a robot by using an A * algorithm, planning a detouring path at the periphery of a hotspot area, and outputting a navigation path sequence. According to the method, intelligent optimization of the motion path of the robot is realized through path planning and an obstacle avoidance strategy, the safety is ensured, the path efficiency is maximized, and unnecessary detour and pause time is reduced.

The invention discloses an obstacle avoidance method and system for an unmanned aerial vehicle, and the method comprises the steps: collecting the three-dimensional point cloud and image information of a laser radar, binocular vision and a millimeter-wave radar, and obtaining an obstacle comprehensive parameter set; carrying out dimensionless processing on the obstacle avoidance feature vector to obtain an obstacle avoidance feature vector; inputting the obstacle avoidance feature vector into a discrimination model constructed based on a double-membership adaptive fuzzy decision tree, and outputting an obstacle avoidance action type according to an obstacle avoidance rule; and calling a corresponding trajectory generator to calculate the vertical speed, the lateral speed and the course instruction of the target in real time, driving a propulsion motor to complete maneuvering, and when the vertical clearance coefficient, the horizontal clearance coefficient and the distance safety margin all exceed preset exit thresholds again, automatically terminating the obstacle avoidance mode and restoring to the original planned route. According to the invention, the problems of low precision and insufficient stability of real-time obstacle avoidance decision making of the unmanned aerial vehicle in a complex environment are effectively solved.

The invention discloses a laminated plate impact event three-dimensional strain field inversion method based on binocular vision, and the method comprises the steps: laying dynamic tracking mark points on the surface of an impacted carbon fiber laminated plate, synchronously collecting the image data of the whole impact process through a high-speed binocular camera, and generating a binocular vision image sequence with impact time sequence information; generating three-dimensional displacement field data of the impact area of the carbon fiber laminated plate based on the binocular vision image sequence; calling the anisotropic constitutive model of the carbon fiber laminated plate to generate a preliminary three-dimensional strain field inversion result; and performing coupling operation on the preliminary three-dimensional strain field inversion result, the impact load parameter and the laminated plate material characteristic parameter to generate a final carbon fiber laminated plate three-dimensional strain field inversion result consistent with the dynamic response of the impact event. According to the embodiment of the invention, accurate full-field representation of the dynamic strain of the impact event can be realized, and reliable data support is provided for impact resistance evaluation and structure optimization of the laminated plate.

The invention provides a binocular vision teleoperation robot system, a robot and a teleoperation method and device, and relates to the technical field of computers. The method comprises the following steps: receiving a binocular video stream obtained by carrying out image acquisition on an actual scene by a binocular vision robot, and rendering the binocular video stream to obtain a three-dimensional scene; displaying the three-dimensional scene to an operator of the binocular vision robot to capture motion data of the operator observing the three-dimensional scene; determining a target area from the actual scene according to the action data, and obtaining a visual control instruction based on the target area; the vision control instruction is transmitted to the binocular vision robot for teleoperation; wherein the visual control instruction is used for adjusting imaging parameters of the binocular vision robot for image acquisition of an actual scene, and the visual direction of the binocular vision robot can be rapidly and naturally kept consistent with the attention point of the operator.

The invention provides a substation safety intelligent monitoring method based on infrared binocular vision, which comprises the following steps: acquiring an infrared radiation image, a visible light image and environmental parameters of equipment, and accurately inverting the real temperature of the equipment after eliminating environmental interference in combination with a depth distance calculation and physical compensation model; a historical time sequence of a real temperature is used to construct a multi-dimensional state vector fusing a thermal feature, a spatial feature and an environment feature, and intelligent classification evaluation of the security risk of the transformer substation is realized through an improved decision tree model. Meanwhile, a time sequence prediction model is introduced to predict the temperature rise trend of the equipment, the remaining time for reaching an early warning threshold value is calculated, and a dynamic grading early warning report is generated. According to the method, the temperature measurement precision and the risk identification accuracy are effectively improved, early warning and active prevention and control of the operation state of the equipment are realized, and the operation safety and the intelligent operation and maintenance level of the transformer substation are remarkably improved.

The invention discloses a deep-sea mining area sediment plume dynamic monitoring system and a multi-module fusion analysis method thereof, and belongs to the technical field of deep-sea environment monitoring and ocean engineering. The system comprises a fixed monitoring unit and a movable monitoring unit, the fixed monitoring unit is composed of an in-situ optical and three-dimensional sonar observation base station, a sediment capturer array and a multi-parameter sensor array and is arranged in a mining area; and the mobile unit comprises an AUV (Autonomous Underwater Vehicle) carrying a multispectral laser radar and an ROV (Remote Operated Vehicle) carrying a binocular vision system and a micro sampler. According to the analysis method, a plume motion field is extracted through multi-source data fusion, dark channel defogging and a pyramid optical flow method, a physical-data dual-drive model is constructed to simulate the plume diffusion and settlement process, a three-dimensional dynamic thermodynamic diagram is generated in real time, and an intelligent early warning threshold value is set based on the diffusion radius and the settlement flux. According to the invention, plume multi-scale, high-precision, real-time and dynamic monitoring and evaluation are realized, and deep-sea mining operation optimization and environment compliance supervision are effectively supported.

The invention discloses an electric arc three-dimensional reconstruction system and method based on binocular vision. The system comprises a double-camera image acquisition module, a correction module, a boundary contour point extraction module and a three-dimensional reconstruction module. The dual-camera image acquisition module completes dual-camera parameter calibration and dual-vision arc image shooting operation at different positions; the correction module corrects an optical distortion area and a breakpoint area of the double-vision arc image to obtain a corrected double-vision arc image; the boundary contour point extraction module adopts a third-order Bezier curve to perform segmentation fitting on the boundary contour of the corrected double-vision arc image to obtain continuous double-vision arc boundary points; and the three-dimensional reconstruction module reconstructs a three-dimensional contour line of the electric arc through a triangulation method according to the camera calibration parameters and the double-vision electric arc boundary points, and generates a corresponding three-dimensional point cloud data set. According to the method, the distortion and breakpoint phenomena of the arc image can be repaired, and high-precision reconstruction of three-dimensional data of the arc is realized.

An ophthalmic lens configured to correct and / or treat at least one condition of the eye (e.g., presbyopia, myopia, hyperopia, astigmatism, binocular vision disorders, night vision image quality, and / or visual fatigue syndrome) comprising: a base power profile; and at least one pulse selected to modify the base power profile.

The invention provides a binocular vision global positioning system and method for a mobile robot, and relates to the technical field of robot vision navigation, and the method comprises the steps: carrying out the adaptive exposure compensation of binocular images of a left camera and a right camera, extracting edge contour feature points irrelevant to illumination, and generating a high-quality depth feature map through the combination of parallax calculation; meanwhile, feature units are constructed according to the inflection points, so that the global position of the mobile robot is determined. Therefore, the problem that the global positioning precision is reduced due to unstable visual feature extraction caused by rapid change of illumination intensity of the mobile robot in a multi-region switching scene can be solved to a certain extent.

The invention provides an AI human shape recognition perception system and method based on binocular vision, and the method comprises the steps: carrying out the real-time collection through a binocular camera when a doorbell key is triggered, and carrying out the preprocessing of an original image collected in real time; performing coarse parallax estimation on the real-time image rectification to obtain a full-field coarse depth map, determining a human shape candidate region list by using the full-field coarse depth map and combining the heat source region of interest, and performing fine parallax estimation on the human shape candidate region list to obtain a fine depth patch; converting the corresponding fine depth patch into a three-dimensional point cloud set according to the pose information, and performing scale prior screening based on the corresponding three-dimensional point cloud set to obtain a plurality of human shape candidate reserved areas; and performing fusion identification according to the extracted multi-modal features to obtain a human shape identification result. According to the technical scheme provided by the invention, layered parallax estimation and three-dimensional scale prior screening can be carried out on the real-time image to realize high-reliability human shape recognition under the condition of low power consumption, so that the recognition reliability of a sensing system is improved.

The invention provides a rapid assembly system and method for in-cabin parts of cylindrical products, and the system is characterized in that a mechanical arm is fixedly mounted through a base, the tail end of the mechanical arm is sequentially provided with a six-dimensional force sensor and a tail end execution device, and the tail end execution device is used for grabbing the to-be-assembled parts and carrying the to-be-assembled parts to a mounting position in a cylindrical section along a preset path; the six-dimensional force sensor is used for acquiring contact force and torque on a tail end load and an assembly path so as to judge the state of the assembly path; the assembly platform is used for supporting and flexibly clamping a cylinder section product, the light source equipment and the binocular vision camera form an image acquisition system, and the binocular vision camera is used for acquiring pose and mark point coordinate information of a cylinder section. According to the method, the problems that during existing automatic assembly, multi-batch and multi-state real-time posture positioning is tedious, coaxial assembly of components in a barrel-section-shaped product cabin is difficult to guarantee, and extrusion collision damage is caused in the assembly process are solved, the operation difficulty and risk are reduced, and the system assembly flexibility is improved.

The invention discloses an underwater image enhancement method and system based on binocular vision and polarization imaging. The method comprises the following steps: firstly, synchronously acquiring an orthogonal polarization image pair through a binocular polarization imaging unit, recovering scene depth information from the polarization image pair by using a binocular stereoscopic vision technology, and calculating a transmissivity graph; analyzing the polarization characteristic difference between target reflected light and backscattered light, establishing a polarization difference model, and separating background scattered light; solving an underwater imaging equation in combination with the depth information and a polarization difference model to obtain a preliminary restoration result of the target reflected light; and finally, further improving the image quality through the steps of multi-scale spectrum analysis, adaptive filter construction and post-processing by adopting a spectrum adaptive image enhancement technology. The system comprises a binocular polarization imaging unit, a calculation processing unit and an active illumination unit. According to the method, the problems of color distortion, low contrast and fuzzy details of the underwater image are solved, and the visual quality and usability of the underwater image are improved.

The invention relates to a vehicle-mounted road defect detection system and method based on binocular vision and deep fusion, and the system comprises a binocular camera module, a main control platform, a positioning module, a communication module and a power supply control module. The system realizes automatic detection and severity quantitative evaluation of various pavement defects such as cracks, pits and ruts, obtains defect positions in combination with a positioning module, and uploads the defect positions to a cloud platform in real time through a communication module, thereby forming a closed-loop urban road defect information perception and management system. According to the method, the quantitative evaluation capability of the identification accuracy and severity of the road defects is remarkably improved, and automatic identification, accurate positioning and real-time cloud synchronous uploading of the road defects are realized; the method has the advantages of flexible deployment, efficient operation, high adaptability and the like, is particularly suitable for urban road intelligent inspection and maintenance management scenes, and has good engineering application prospects and popularization values.

The invention discloses an AGV-six-degree-of-freedom mechanical arm operating system and method based on double-vision fusion, the system comprises a control system and a six-degree-of-freedom mechanical arm, and the control system comprises a master control unit, a slave control unit and a dynamic error compensation module; the method comprises the steps of AGV autonomous navigation and material transfer, cross-station accurate docking and dynamic error compensation, high-precision biological medicine operation under double-vision fusion guidance, and material recovery and circulation. According to the invention, through dual-vision dynamic fusion of eyes outside the hand and eyes on the hand, adaptive switching from global coarse positioning to local fine positioning is realized; due to the integrated design of the AGV and the six-degree-of-freedom mechanical arm, the operation flexibility is greatly improved; an extended Kalman filtering dynamic error compensation algorithm is innovatively adopted, so that the comprehensive error of the AGV, the mechanical arm and the visual system can be corrected in real time; and by combining with a double-layer distributed control architecture, the system realizes multi-task intelligent scheduling and modular collaboration.

The invention discloses a multi-source information fusion-based tunneling equipment vision-assisted pose detection system and method, and relates to the technical field of exploration and engineering surveying, and the system comprises a binocular vision module, a strapdown inertial navigation module, a laser orientation instrument module and a data fusion processing module. The binocular vision module solves three-dimensional coordinates through stereoscopic vision; the strapdown inertial navigation module provides high-frequency dynamic attitude data; the laser orientation instrument modules are triangularly arranged to provide a global absolute position reference; and the data fusion processing module adopts an extended Kalman filtering dynamic fusion algorithm and combines dynamic weight adjustment to fuse multi-source data. The method comprises the steps of data acquisition, preprocessing and time-space synchronization, fusion calculation and pose information output. Through the multi-source collaborative fusion and dynamic environment adaptation technology, the detection precision and stability are improved, the automatic tracking and remote monitoring requirements of the tunneling equipment are met, and intelligent upgrading of coal mine tunneling is promoted.

The invention relates to a wall-climbing robot system for detecting a coating of a steel pipe truss structure and an application method, and the system comprises a self-adaptive magnetic adsorption mobile platform which can be adaptively and stably adsorbed on steel trusses with different pipe diameters to execute an inspection task; the robot platform is provided with an autonomous positioning navigation system composed of a laser radar, binocular vision and an airborne computer, and the position of the robot is positioned in the steel pipe truss without satellite signals through real-time mapping and a positioning algorithm of tight coupling of laser and vision. The front part of the robot is provided with an electrically foldable surrounding type array camera, so that full-surface coverage type shooting of a steel pipe component is realized, and real-time identification of coating apparent defects is executed in combination with an artificial intelligence disease identification algorithm in an airborne computer; and the lower part of the robot is provided with a telescopic elastic electromagnetic eddy current probe for detecting the thickness of the coating. The method has the advantages that the apparent diseases and the thicknesses of coatings of steel structures such as ceilings of high-speed rail stations and ceilings of large stadiums can be rapidly detected.

The invention discloses a power transmission channel obstacle positioning method and system based on binocular vision and COLMAP, and relates to the technical field of intelligent inspection and three-dimensional space perception, and the method comprises the steps: collecting a left image pair and a right image pair of a power transmission channel, carrying out the preprocessing of denoising, distortion correction and image enhancement, generating a depth map through parallax calculation based on the preprocessed image pair, and carrying out the positioning of the obstacle in the power transmission channel. Performing feature extraction and matching, sparse point cloud generation and dense point cloud reconstruction, outputting a three-dimensional dense point cloud model of the power transmission channel, segmenting an obstacle region from the dense point cloud model, and calculating the spatial position and size of an obstacle and the distance between the obstacle and the power transmission line through geometric model fitting. And the obstacle risk level is evaluated according to the distance and the size to generate a positioning result. According to the invention, full-process automation from data acquisition to risk early warning is realized, the problems of low efficiency and large error of traditional manual inspection are solved, and reliable technical support is provided for safe operation and maintenance of the power transmission line.

The invention discloses a method and system for detecting the bucket fullness rate of a shovel loading robot based on multi-source fusion, and belongs to the technical field of shovel loading robos.The working state of a working device at the front end of the shovel loading robot is monitored, and when it is detected that a swing mechanism of the shovel loading robot starts to work, system detection work is started; performing calibration fusion processing on the point cloud data of the 4D millimeter wave radar and the binocular vision camera to obtain high-quality bucket point cloud data; the working process between the 4D millimeter wave radar and the binocular vision camera is self-calibrated; and according to the obtained high-quality bucket point cloud data, processing and segmenting to obtain the point cloud and the fullness rate of the excavated material. The method can accurately identify the outline of the bucket and the morphology and volume of materials in the bucket, and improves the autonomous operation efficiency.

The invention discloses a visual analysis method and equipment for the trafficability of a grain transport vehicle tractor track and a storage medium. According to the method, images are collected through a vehicle-mounted binocular vision system, and multi-dimensional geometric attributes of the terrain and obstacles are automatically extracted through semantic segmentation, three-dimensional reconstruction and voxel modeling. And on the basis of the extracted multi-dimensional attributes, constructing a hierarchical evaluation system containing terrain and obstacle criteria. A local weight of each attribute index is determined by adopting an analytic hierarchy process, and a fuzzy membership degree vector of each index on different passage evaluation grades is constructed according to dynamic judgment parameters adaptively set with the current grain transport vehicle model number and the terrain, so that a membership degree matrix is formed. And finally, through hierarchical fuzzy comprehensive evaluation, calculating to obtain a comprehensive trafficability score. The accuracy, reliability and automation level of tractor track trafficability evaluation are improved.

The utility model discloses a binocular vision assembly with adjustable base line spacing, comprising a track, the track is provided with an upward protruding guide rail and sliding grooves located at two sides of the guide rail, the track is slidably provided with two supports, the supports are in sliding fit with the guide rail, and the guide rail is provided with a base line. An adjusting piece used for adjusting the sliding state of the support is arranged in the sliding groove. One side face of the track is provided with baseline scales used for measuring the distance, and the support is provided with an identification baseline. Binocular cameras with consistent image acquisition directions are mounted on the brackets; the distance adjustment is carried out under the same reference condition, and the accuracy of distance adjustment is met.