1375 results about "Binocular single vision" patented technology

The invention relates to the technical field of geological disaster monitoring and early warning, in particular to a side slope slippage monitoring and early warning method based on an image recognition technology, which comprises the following steps: arranging a monitoring target and a reference target in a to-be-monitored area of a side slope, arranging two image displacement monitoring devices at opposite stable positions at equal height to acquire two-dimensional displacement data of the targets; after error correction, an equipment monitoring coordinate system included angle is calculated based on parameters such as equipment spacing, slope inclination displacement is calculated through vector synthesis, and three-dimensional slippage deformation is calculated in combination with vertical displacement; meanwhile, a multi-level early warning mechanism based on the slip rate and the accumulated slip amount is established, a Delaunay triangulation network is constructed, and an improved adaptive Kriging interpolation algorithm is adopted to realize deformation trend prediction. According to the method, binocular vision and multi-algorithm fusion are utilized to realize high-precision three-dimensional monitoring, the evaluation accuracy is improved through the dynamic weighting model and intelligent early warning, the method has the advantages of high monitoring precision, timely early warning, high adaptability and the like, and the safety of slope engineering can be effectively guaranteed.

The invention relates to the technical field of robot autonomous navigation and control, and discloses a robot system based on a sensing system, which comprises a multi-mode sensing module for outputting data to a dynamic environment modeling module; the dynamic environment modeling module is used for transmitting the model to the path planning module; the path planning module outputs the path sequence to the neural motion control module; the neural motion control module outputs the control signal to the dynamic execution module; the dynamic execution module is used for receiving the pulse control signal and driving each joint of the robot to execute the path in combination with inverse kinematics solution and a feedback control mechanism; and the collaborative optimization module is in two-way communication with the modeling module, the path planning module and the neural motion control module. According to the method, the contradiction between real-time performance and integrity of environment modeling in a complex scene is solved by fusing cross-modal information complementary characteristics of binocular vision, millimeter-wave radar and inertial data and combining mathematical representation of a hypergraph topology model on a dynamic obstacle interaction relationship.

The invention discloses an underwater binocular positioning method and device based on target assistance and a storage medium, and belongs to the technical field of underwater environment perception, and the method comprises the following steps: obtaining internal and external parameters of a binocular camera; solving to obtain refraction parameters of the binocular camera; the binocular camera is controlled to shoot and collect the target image, an initial underwater cooperative positioning target image is obtained, the initial underwater cooperative positioning target image is corrected, and a corrected underwater cooperative positioning target image is obtained; a YOLOv8 model is adopted to detect a target, and an area where the target is located in the underwater cooperative positioning target image is selected; feature points are extracted and matched through an ORB algorithm, and matched feature points are obtained; and calculating to obtain the three-dimensional coordinate of the target in the camera coordinate system. According to the invention, accurate positioning of the underwater target can be realized through a binocular vision method, and working requirements in a complex underwater environment are met.

The invention relates to the technical field of intelligent agricultural equipment, and discloses an automatic agaricus bisporus picking method, device and equipment based on artificial intelligence and a medium. The method comprises the steps that RGB images and three-dimensional point cloud data of the agaricus bisporus growth environment are collected in real time through a binocular vision camera and a depth sensor which are installed at the tail end of a picking robot, and environment illumination parameters and cultivation bed coordinate information are obtained; inputting the RGB image into a pre-trained lightweight convolutional neural network, identifying a pileus contour, a stipe position and a maturity level of the agaricus bisporus, calculating a space coordinate, a height and a growth inclination angle of the agaricus bisporus based on the three-dimensional point cloud data, and constructing an agaricus bisporus target database; a mechanical arm picking path is generated based on the agaricus bisporus target database, and path weight parameters are optimized online through a preset reinforcement learning model; according to the maturity grade and morphological characteristics of the agaricus bisporus, the clamping force and the rotating angle of the end effector are dynamically adjusted, and automatic picking of the agaricus bisporus is completed.

The invention discloses a pig individual identification and health monitoring system based on biological characteristics, and relates to the technical field of medical treatment, and the system comprises a multi-mode biological characteristic collection module which is used for obtaining pig 3D facial characteristics, ear blood vessel distribution characteristics, gait dynamic characteristics and physiological data; the biological feature fusion recognition module is used for fusing multi-modal features based on a Transform architecture and realizing individual unique identification and feature template dynamic updating; and the digital twinborn health monitoring module is used for constructing a pig full-life-cycle digital twinborn model and realizing health state evaluation and disease early warning in combination with an LSTM neural network. According to the pig individual recognition and health monitoring system based on the biological characteristics, 1024-dimensional standardized feature vectors are constructed through cooperation of multiple devices such as an RGB-D camera, a near-infrared camera and a binocular vision sensor, modal weights are dynamically distributed in combination with a Transform network, and high-precision recognition in a complex scene is achieved.

The invention belongs to the technical field of ocean observation, and discloses an ocean wave field reconstruction method and device based on binocular vision. An ocean wave field reconstruction method based on binocular vision comprises the steps that internal parameters of a camera are calibrated through a Bouguet calibration algorithm, a binocular camera provided with an IMU sensor is used for collecting images and pose information, feature points are extracted through an SURF algorithm, feature matching is completed under a game theory framework, and abnormal points are removed through an RANSAC algorithm. Then, camera parameters are calculated based on an eight-point method, and the camera parameters and three-dimensional point coordinates are optimized through an SBA algorithm; thirdly, generating a disparity map by adopting epipolar correction and an SGM algorithm, and performing morphological processing on the disparity map; and the processed image is subjected to triangulation to generate a three-dimensional point cloud, and filtering is carried out. And finally, estimating a sea level position through an RANSAC algorithm, and optimizing a result through a least square method to complete three-dimensional reconstruction of ocean waves.

The invention discloses a robot control method and system for an industrial pipeline, and relates to the technical field of robot control. The method comprises the steps that a flange plate is installed on an end effector of the robot, a binocular vision camera is installed in the center of the front end of the flange plate, and four point laser displacement sensors are symmetrically arranged on the flange plate at different heights; the binocular vision camera collects a pipeline image; the upper computer determines an initial pose control result of the robot according to the pipeline image so as to control the robot to move preliminarily; the four point laser displacement sensors are used for collecting spatial displacement data of the pipeline at different height points after the robot moves preliminarily; and the upper computer determines a target pose control result of the end effector of the robot through the spatial displacement data so as to control the end effector of the robot to carry out pose adjustment. According to the method, accurate control over the axial parallelism of the robot end effector and the pipeline can be achieved, and therefore the pipeline assembling accuracy is improved.

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 a seismic isolation structure support three-dimensional deformation monitoring method and system based on binocular vision. The method comprises the steps that a binocular camera is adopted to collect monitoring scenes under the view angle of a left camera and the view angle of a right camera; performing image distortion removal and three-dimensional correction on the monitored image pair by adopting camera internal and external parameters obtained by camera calibration; carrying out target detection on the corrected image by adopting a target detection algorithm; according to the coordinates of the target detection frame, calculating a cutting frame and cutting target areas in the left image and the right image; carrying out stereo matching on the cut image pair; according to the disparity value of each pixel in the disparity map, depth information of the target is calculated through a triangulation method, and finally a three-dimensional dense point cloud of the target is generated; performing optimization processing on the target three-dimensional dense point cloud obtained in the previous step to obtain an optimized point cloud; solving a rigid body transformation matrix of the two-point clouds by using an iterative nearest point algorithm; according to the invention, the cost of monitoring the three-dimensional pose deformation of the rubber support can be reduced, and the robustness and precision are effectively improved.

The invention discloses a collaborative unmanned aerial vehicle cluster path planning and scheduling system, and particularly relates to the technical field of unmanned aerial vehicle intelligent control, and the system comprises a multi-mode sensing unit which is composed of a heterogeneous sensor array composed of LiDAR, binocular vision and millimeter wave radar, and an output dynamically updated three-dimensional Gaussian mixture map; the decision control unit is used for implementing double-layer optimization of mixed integer programming task allocation and artificial potential field path planning; the dynamic communication network adopts a hybrid networking protocol of TDMA backbone nodes and 802.11 ax terminal nodes; an energy management module; aiming at the insufficient environment perception and dynamic modeling capability in the prior art, the method achieves the effects that the centimeter-level positioning precision and the dynamic obstacle recognition rate are greater than 92%, the environment model is delayed and compressed to be within 200ms, the response speed is increased by 5 times by setting multi-modal sensor fusion, constructing a dynamically updated 3D Gaussian mixture map and combining an LSTM network to predict the obstacle trajectory in real time, and the dynamic obstacle recognition rate is greater than 92%. And the obstacle avoidance reliability in a complex scene is obviously enhanced.

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 provides a narrow-space-oriented truck-mounted crane manual arm operation path planning method and system, and relates to the technical field of path planning, and the method comprises the steps: carrying out the multi-source information fusion through binocular vision and laser ranging, and building an operation space three-dimensional model; a convolutional neural network is adopted to identify an obstacle, a kinematics constraint is combined to establish an obstacle avoidance cost function, and a bidirectional probability sampling algorithm is utilized to generate and optimize an initial path. Afterwards, a target path is screened by considering the dynamic constraint of the load weight, and dynamic adjustment is carried out based on a comprehensive evaluation function of path length, smoothness and safety degree and a virtual spring damping model; and finally, performing polynomial fitting on the adjusted path, calculating a joint angle and performing local correction according to a kinematic model, and finally outputting a working path meeting constraint conditions. According to the invention, the working efficiency and safety of the truck-mounted crane in a narrow space are effectively improved.

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 a pair of blind person intelligent glasses based on binocular vision. The glasses comprise a glasses terminal and a cloud end. A data acquisition and transmission module, a voice interaction and feedback module and a power supply system are arranged in the glasses terminal, and the glasses terminal is used for acquiring and processing data and receiving and feeding back voice instructions; the cloud integrates a sensing module, a task planning module, an action module and a path planning and navigation module, the sensing module analyzes instructions and environment information, the task planning module extracts natural language intentions and generates task structures, the action module dynamically generates task templates to execute tasks, and the path planning and navigation module achieves fine-grained obstacle avoidance navigation. The environmental perception precision is improved through binocular vision and GPS fusion, the use threshold is reduced through natural language interaction, the complex task processing capacity of the system is improved through dynamic task decomposition, and the obstacle avoidance accuracy is improved through hierarchical path planning.

The invention provides a binocular vision inertial odometer method based on a direct method in a dynamic environment, and the method comprises the steps: obtaining a prior segmentation mask of a potential dynamic object through a Mask R-CNN instance segmentation network, and judging the real dynamics of the object through a sparse optical flow method. According to self-adaptive dynamic thresholds of front and back frame optical flow changes and included angles of a potential dynamic object, features of a potential dynamic area are distinguished, features extracted on the dynamic object are removed, and residual static feature points are tracked to estimate an initial pose; in direct method coarse tracking, binocular vision information and IMU information are tightly coupled, and a residual equation of luminosity and IMU is jointly created; and combining IMU error information in a rear-end sliding window, constructing a luminosity residual error by adding a method of projecting feature points of a left-view view of a dominant frame to a right-view view of a target frame, and carrying out iterative updating optimization on luminosity parameters and pose changes.

The invention provides an automatic deployment method for a robot system, and the method comprises the steps: collecting three-dimensional space data of a target environment through a multi-mode sensor array which comprises a laser radar, a binocular vision camera and an inertial measurement unit; fusing sensor data based on a space-time synchronization algorithm to generate an environment feature matrix; analyzing a user task instruction, extracting a task target and decomposing the task target into time, path priority and safety threshold parameters; constructing a multi-objective optimization model, and generating a candidate path set; adopting an improved Pareto leading edge screening algorithm to select an optimal path sequence; executing virtual verification and adjusting path deviation in the digital twin environment; and the physical execution mechanism is driven to implement deployment operation, and the local path re-planning module is triggered when the deviation is detected to exceed a threshold value. According to the invention, the accuracy, safety and efficiency of path planning can be improved, and the energy consumption and time cost are reduced.

The invention provides a movable flexible docking platform, method and equipment based on visual guidance and a storage medium, and relates to the field of automatic auxiliary assembly. The docking platform comprises an AGV trolley, a six-degree-of-freedom platform erected in the middle of the upper portion of the AGV trolley, and a binocular vision system erected on the rear side of the upper portion of the AGV trolley. The binocular vision system provides real-time vision guidance in the advancing process of the AGV; a butt joint workpiece A is placed on a six-degree-of-freedom platform, and a butt joint workpiece B which has an assembly relation with the butt joint workpiece A is placed on a working table at a target position; and the AGV transports the butt joint workpiece A to the target position, and butt joint of the butt joint workpiece A and the butt joint workpiece B is completed. According to the invention, the six-degree-of-freedom parallel platform is arranged on the AGV trolley, so that the pose fine adjustment of the assembled workpiece is realized, the pose of the workpiece can be continuously adjusted in the transportation process to keep balance, and the workpiece transportation and high-precision butt joint can be realized.

The invention provides an adaptive welding seam detection and three-dimensional reconstruction method based on deep learning and binocular vision. The adaptive welding seam detection and three-dimensional reconstruction method comprises the steps of S1, collecting samples and making a training data set; s2, the picture of the sample to be welded is processed, a feature region is recognized, the image quality of the region to be welded is analyzed and evaluated through wavelet transform and local variance, and the noise level and the contrast ratio are calculated; s3, dynamically generating edge detection parameters and model fitting parameters according to the image quality; s4, using an edge detection algorithm to extract edge point cloud of the welding seam area; s5, performing RANSAC linear fitting, weighted least square fitting and polynomial curve fitting on the edge point cloud in parallel; s6, selecting an optimal fitting result based on an image quality adaptive dynamic scoring model; and S7, carrying out three-dimensional coordinate conversion in combination with the three-dimensional matching model IGEV-Stereo, and outputting a final welding seam three-dimensional coordinate. According to the invention, automatic detection of the position and size of the welding seam can be efficiently and accurately realized.

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.

A slope monitoring method based on optical flow estimation and binocular vision belongs to the technical field of deformation monitoring and measurement, and adopts the technical scheme that a binocular stereoscopic vision system is built, and camera acquisition parameters are calibrated; transmitting the image to a server in real time through the Internet of Things; distortion correction and stereo matching are carried out to obtain a depth map; monitoring points are manually or automatically arranged on the corrected image, and dense optical flow estimation tracking pixel point movement is carried out; world coordinates of observation points and movement conditions of each frame are obtained, and displacement changes of slope monitoring points are obtained by combining coordinates before and after displacement; and obtaining a displacement rate by combining monitoring time and displacement, judging slope safety, obtaining a displacement track, and performing linear interpolation to obtain a slope deformation cloud picture, thereby realizing the monitoring purpose. The slope displacement monitoring method has the advantages that slope monitoring can be carried out more flexibly and efficiently, the safety of workers is improved, the slope monitoring cost is reduced, real-time slope displacement monitoring is achieved, and the intensive degree of slope monitoring points is greatly 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 technical field of unmanned aerial vehicles, and particularly discloses a high-precision small unmanned aerial vehicle real-time tracking method which comprises the steps that a multi-modal sensor array is constructed through a millimeter wave radar, a binocular vision module and an infrared thermal imager, and the multi-modal sensor array is used for collecting three-dimensional motion data of a target object in real time; constructing a fusion tracking model based on improved Kalman filtering by using the collected three-dimensional motion data, dynamically distributing the weight of a multi-source sensor through a convolution attention mechanism, and compensating a motion trail prediction error by using an adaptive noise covariance matrix; through cooperative work of the millimeter wave radar, the binocular vision module and the infrared thermal imager, challenges such as illumination change and target shielding can be effectively handled, three-dimensional motion data of a target object are collected in real time, and the target positioning and tracking precision is improved. Meanwhile, the improved Kalman filtering algorithm is combined with a convolution attention mechanism, so that the weight of the sensor can be dynamically adjusted, and a motion track prediction error is compensated.

The invention relates to the technical field of intelligent travel control of walking-aided robots, and discloses a multi-sensor-based obstacle avoidance control method and system for a walking-aided robot. The obstacle avoidance control method for the walking-aid robot is applied to walking-aid robot equipment, and specifically comprises the following steps: S101, completing the installation of a multi-sensor module on the walking-aid robot, and collecting multi-source data obtained based on multiple sensors in real time, unifying the multi-source data to a base coordinate system of the walking aid robot by adopting timestamp synchronization and a coordinate system conversion matrix, and generating a fused dynamic obstacle sequence map; and S102, on the basis of the dynamic obstacle sequence map, a Kalman filtering and LSTM neural network fusion model is adopted to predict the motion trajectory of the dynamic obstacle. According to the method, the environment perception and decision-making architecture of the walking-aid robot is reconstructed, a multi-sensor space-time cooperation mechanism is matched through cross-modal data of laser radar and binocular vision, and ultrasonic sensor low obstacle special detection is combined, so that the step recognition rate is greatly improved.

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 is suitable for the technical field of electrical equipment corrosion detection, and provides an electrical equipment corrosion detection method based on double-spectrum collaborative analysis and space-time prediction, and the method comprises the following steps: carrying out fusion analysis on a visible light image and an infrared image of acquired electrical equipment, and outputting position information and morphological category of a corrosion region; a binocular vision system and an IMU sensor are combined to calculate three-dimensional space coordinates, main direction angles and environmental parameters of the corrosion area, and pose data are generated; inputting the pose data into a preset corrosion extension path prediction model for processing, and outputting physical characteristic parameters; fusing the dual-frequency RTK GPS positioning data, the IMU attitude data and the physical characteristic parameters to construct a space-time association data set, and inputting the space-time association data set into a preset space-time diagram neural network model to analyze a space-time evolution relationship of diagram structure modeling corrosion nodes; and outputting microscopic and macroscopic prediction results. The identification accuracy of corrosion characteristics and the corrosion detection efficiency can be improved, and the labor cost and the operation risk are reduced.

The invention belongs to the technical field of humanoid robot control, and particularly relates to a humanoid double-arm robot valve tightening method and system, and the method comprises the steps: obtaining a valve image data set based on a binocular vision camera; labeling the valve image data set to generate a labeled data set; performing enhancement processing on the annotation data; recognizing and positioning the valve based on a preset binocular vision algorithm and YOLO, and recognizing the position, the frame and the center point of the valve; the two-arm robot is moved to the two sides of the valve, and the positions and poses of the tail ends of the two arms in other directions except the clamping direction are kept consistent; clamping control is carried out based on a self-adaptive variable admittance controller; the valve is driven to rotate through rotation of the joint at the tail end of the double-arm robot, so that the valve is tightened. According to the invention, rapid and accurate valve identification is realized, and the reliability and flexibility of the system in practical application are greatly enhanced.

The invention belongs to a synchronous localization and mapping (SLAM) method in the field of robots, and discloses a binocular vision SLAM method based on a deep learning feature extraction and matching algorithm. The system comprises three modules: a front-end tracking module, a local mapping module and a loopback detection module. For each frame of input binocular image, firstly, feature points of the image are extracted, and then frame-to-frame matching is used to track a current image frame and determine whether the current frame is set as a key frame. In the local mapping module, matching from a key frame to a local map is used to obtain a more accurate pose and a global map, and in addition, the loopback detection module is used for inhibiting accumulative errors of a large scene. Experiments on a public data set and a data set collected by a robot platform show that robust, accurate and globally consistent pose estimation and mapping are realized by the method, and quick real-time operation can be realized on an embedded platform at a speed exceeding 10 FPS.

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 relates to the technical field of pavement flatness detection, in particular to a pavement flatness detection method and system based on binocular vision, and the method comprises the steps: obtaining the pavement point cloud data of a to-be-detected road through a point cloud data obtaining module of the system; analyzing features of the road surface point cloud data, and constructing a comprehensive feature value of each super voxel in the road surface point cloud data; the supervoxels are distinguished by using the comprehensive feature values of all the supervoxels in the road surface point cloud data, and downsampling processing is performed according to the distinguished supervoxels; plane fitting is carried out on the road surface point cloud data after downsampling processing, and a fitting plane and an outlier set are output; and taking the average value of the shortest distances from all the outliers in the outlier set to the fitting plane as the pavement flatness of the road to be measured. According to the method, different downsampling methods are adopted for different areas in the road surface point cloud data, and the precision of road surface flatness detection is improved.