145 results about "Calibration matrix" patented technology

The invention discloses a temperature control management method for an electric oven, and relates to the technical field of intelligent household appliance control, and the method comprises the steps: carrying out the offset correction of a pre-stored mode mapping table based on a calibration matrix, loading a target temperature interval and a three-dimensional temperature field partition parameter according to a cooking mode selected by a user, and triggering a dynamic learning mark; according to the dynamic learning mark, the furnace chamber temperature and the environment temperature change rate are collected in real time, the power compensation amount is calculated through a thermal inertia compensation model, and a partition power control matrix is generated in combination with the three-dimensional temperature field partition parameters; and distributing the power compensation amount and the partition power control matrix to the heating pipe according to a preset topology, dynamically adjusting power output, and implementing temperature early warning and no-load identification based on an acoustic emission spectrum entropy value. The temperature gradient change rate is calculated through the Fourier heat conduction law, the three-dimensional temperature field partition parameters are dynamically generated, and accurate control over the temperature field in the complex three-dimensional space is ensured so as to cope with environment changes and user demand changes.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal data fusion processing method and system, and the method comprises the steps: collecting a multi-modal data set containing the activities of the old through an intelligent old-age care robot; constructing a timestamp calibration matrix based on the multi-modal data set, and carrying out dynamic weight correction on the timestamp calibration matrix by adopting a three-modal data envelope delay compensation algorithm to obtain a three-modal data stream with a synchronous time sequence; performing hierarchical feature extraction through a nested feature extractor to obtain a three-mode tumble feature vector group; performing vector fusion on the three-mode tumble feature vector group to obtain tumble fusion feature vectors; and inputting the tumble fusion feature vector into a multi-modal forward prediction model to perform tumble risk trajectory prediction, and outputting a tumble detection result, the method realizes prediction analysis of body posture changes in a key time window before the old man tumble, and improves the ability to distinguish a slow getting-up action and a real tumble behavior of the old man.

The invention relates to the technical field of nondestructive testing, solves the problems of insufficient flaw detection precision and adaptability of a metal component caused by restriction in the prior art, and particularly discloses a flaw detection system and method for the metal component. The flaw detection system comprises a multi-modal sensor group, a cross-modal feature alignment module, a self-adaptive weight distribution module, a defect decision module and a closed-loop control unit, the flaw detection method comprises the following steps: S100, synchronously acquiring data through the multi-mode sensor group; s200, performing space-time registration on the multi-source data of the metal component by using the dynamic calibration matrix; s300, evaluating the confidence coefficient of the sensor in real time, and generating a defect feature map; s400, inputting the defect feature map into a defect classification network, matching a defect map database, and outputting a defect type and a quantization parameter; and S500, automatically adjusting the detection motion track and the sensor according to the defect parameters. According to the multi-method combined detection for the metal component, the detection precision is improved, and a detection result with higher accuracy can be obtained through comprehensive evaluation according to multi-source data.

The invention relates to a saline intrusion measurement method, system and equipment based on dynamic ship motion conditions and a medium, and aims to solve the technical problem that the salinity inversion precision is reduced due to spatial-temporal dislocation between particle image velocity measurement flow field data and plane laser-induced fluorescence concentration field data caused by ship six-degree-of-freedom motion. According to the method, future poses are predicted by collecting ship motion parameters and environment disturbance parameters, and space-time compensation parameters are generated in combination with a static calibration matrix; performing three-dimensional coordinate transformation and time synchronization on synchronously acquired original flow field and concentration field data by applying compensation parameters, and eliminating space-time deviation caused by ship movement; a three-dimensional salinity distribution field is calculated based on registration data fusion flow field diffusion characteristics and concentration field distribution; and meanwhile, the pose of the measuring equipment is dynamically adjusted through the closed-loop controller so as to optimize the data acquisition quality. According to the method, ship motion interference is effectively inhibited, and accurate characterization of saline invasion frontal surface spatial characteristics is guaranteed.

The invention discloses a transformer substation intelligent patrol recognition algorithm optimization method based on multi-modal fusion, and belongs to the technical field of transformer substation patrol, and the method comprises the following steps: synchronously obtaining a visible light image, an infrared thermal imaging spectrum, an ultrasonic partial discharge signal and environmental parameters, and building a four-dimensional space-time calibration matrix; a dual-channel attention mechanism is adopted to realize multi-source data fusion, a spatial alignment channel and a feature enhancement channel are included, the spatial alignment channel realizes pixel-level registration of visible light and infrared images through affine transformation, and the feature enhancement channel utilizes a graph neural network to construct topological correlation mapping of acoustic, thermal and optical features; constructing a three-stage training architecture containing noise injection; and adding an adversarial generative network in a preprocessing layer to simulate rain fog and electromagnetic interference environments. According to the method, the identification threshold is updated online by deploying a sliding time window mechanism in an output layer, and the problems of poor feature extraction robustness and high false alarm rate of a traditional algorithm in complex environments such as strong electromagnetic fields and rain and fog are solved.

The invention discloses a multi-source 5G message video cooperative monitoring method and system, particularly relates to the technical field of video monitoring, and is used for solving the problems of multi-camera calibration parameter misalignment and video fusion dislocation caused by thermal deformation asynchronism in the prior art. A temperature sensor is arranged on an industrial camera supporting structure, a thermal deformation three-dimensional displacement compensation vector is calculated, a preferential transmission object is marked according to the displacement gradient of an adjacent camera and a material heat conductivity coefficient threshold value, and the displacement vector is transmitted to an edge calculation node according to the priority through a 5G message. All displacement data are fused at a node end to dynamically correct space calibration parameters, a collaborative calibration matrix is generated, and finally, the matrix is utilized to perform real-time registration on multiple paths of video streams and perform workpiece defect detection, so that the geometric consistency of a multi-camera system in a thermal deformation environment is guaranteed, and the visual detection precision and stability are improved.

The invention belongs to the technical field of medical robots, and discloses a surgical robot accurate pose real-time registration method and system based on multi-sensor fusion, and the system comprises a multi-source sensing collection assembly; a time-space calibration mechanism; fusing a resolving platform and an exception elimination platform; a deformation sensing and dynamic compensation device; and a registration result closed-loop feedback unit. According to the invention, a multi-source sensing acquisition assembly and a synchronous control board are integrated on a hardware level, a nanosecond timestamp is generated by using a high-precision clock driven by an onboard crystal oscillator, and a cross-correlation delay calculation chip is combined to execute a cross-correlation function method delay estimation formula, so that microsecond-level accurate correction of delay amount among sensing sources is realized; the coordinate transformation unit automatically selects a matched calibration matrix according to a correction result, multi-source data such as optical positioning, inertial measurement, a torque sensor, ultrasonic or electromagnetic positioning and the like are unified to a global rectangular coordinate system, and the structure can effectively ensure the consistency of time and space references of data output by different sensors.

The invention discloses a DTG printing method and system based on line scanning camera visual deformation and rigid body positioning, and relates to the technical field of digital textile printing. Dynamic images are collected in real time through a line scanning camera, and image coordinates are mapped to a printing equipment world coordinate system through a coordinate calibration module in combination with a calibration matrix; real-time error compensation is used for dynamically eliminating coordinate offset, and a dual-mode positioning module intelligently switches a positioning strategy; rigid body positioning realizes accurate positioning of non-deformation materials through template matching; deformation positioning performs adaptive compensation based on feature point extraction and grid deformation analysis, an image mapping module inversely corrects target pattern deformation according to a positioning result to generate a high-precision TIF image, the high-precision TIF image is converted into a printing instruction through an RIP processing module, and an integrated control module performs one-key triggering and dynamic parameter configuration and performs printing on a target pattern. The collection efficiency is optimized through light source synchronization and self-adaptive triggering, the system stability is improved in combination with a template database and an exception handling module, and high-quality printing of complex deformation materials is achieved.

According to the robot vision calibration device and method, through collaborative design of the industrial personal computer, the vision system, the robot, the calibration jig and the calibration plate, the problems caused by complex structure, low calibration precision and poor adaptability in the prior art are solved. A floating mechanism of the calibration jig is combined with a displacement sensor to realize multi-degree-of-freedom dynamic displacement compensation, so that mechanical positioning errors are remarkably reduced; the conical surface guide structure and the reference hole layout of the calibration plate optimize the stability of the positioning reference, and ensure the accurate embedding of the calibration needle. The flexible installation of the visual system adapts to different scene requirements. According to the calibration method, the generation of a high-precision visual calibration matrix is realized through the processes of multi-reference hole coordinate recording, calibration ring coordinate calculation and visual image acquisition and matching. According to the whole scheme, through a dynamic compensation mechanism and intelligent process design, the calibration precision, efficiency and robustness are improved, meanwhile, the operation complexity is reduced, and reliable technical support is provided for an industrial automation scene.

The invention discloses a three-dimensional calibration automatic system suitable for a six-axis physiotherapy robot, and relates to the technical field of robot vision calibration and automatic control, and the system comprises an equipment communication management module which automatically identifies and distributes connection equipment through an intelligent detection technology, and outputs an equipment control instruction; the data acquisition and processing module is used for identifying 13-point feature pixels through RGB information, performing deep matching, calculating 13-point theoretical feature three-dimensional information and outputting an optimized calibration matrix MB2R in combination with a transformation relation between the camera and the tail end of the mechanical arm and a base coordinate transformation relation; and the result verification and backup uploading module is used for converting the relative position of the three-dimensional feature platform and the robot, acquiring three-dimensional information of an evaluation point, controlling the mechanical arm to move to an optimization point position, and calculating a pixel difference value and a height difference value. The method has the advantages that full-automatic millimeter-level calibration precision is achieved through a collaborative structure of multi-protocol heterogeneous equipment intelligent integration, iterative optimization algorithm fitting and closed-loop double-index verification.

The invention discloses a mechanical arm intelligent grabbing system and method based on three-dimensional vision. The system comprises a three-dimensional vision system, an image preprocessing module, a model matching module, a grabbing point generation module, a grabbing control module and a dynamic environment self-adaption module. The method comprises the steps of multi-modal reference model collection and feature library establishment, precise hand-eye calibration and grabbing strategy self-learning and multi-modal fusion recognition, and dynamic prediction and self-adaptive grabbing. By adopting the three-dimensional visual recognition system and combining algorithms such as point cloud processing, point cloud method phase generation, point cloud downsampling, rough model matching and fine model matching, the limitation that a traditional mechanical arm system depends on two-dimensional image training and position coordinate point matching is overcome; by generating the grabbing points and calculating the pose coordinates of the grabbing points according to the hand-eye calibration matrix, the problem that the estimated pose of the grabbed object under the camera coordinate system is inaccurate in the prior art is solved. The system is easy to implement and has high practicability and popularization value.

The embodiment of the invention provides a robot control method and system based on virtual reality and hand motion capture, and the method comprises the steps: collecting the hand posture data of a user, obtaining the first hand posture data of the user through a virtual reality device, the first hand posture data comprises the hand end posture information, and obtaining the first hand posture data of the user through a virtual reality device; obtaining second hand posture data of the user through wearable hand motion capture equipment, wherein the second hand posture data comprises hand joint fine motion information; standardizing the hand position and posture data, and mapping the hand position and posture data into robot target motion data based on a preset calibration matrix, the robot target motion data including first robot target motion data mapped based on the first hand position and posture data and second robot target motion data mapped based on the second hand position and posture data; robot motion control data is generated based on the robot target motion data, and robot parts are controlled to move. According to the robot control method and system based on virtual reality and hand motion capture, the operation precision of the robot can be greatly improved, the operation real-time performance and smoothness are enhanced, and the motion state of the robot can be monitored in real time.

Techniques are provided for real-time calibration of an eye-tracking system in a wearable display device using user interactions with a graphical user interface. A background calibration process continuously or intermittently updates a calibration matrix based on stable gaze-target pairs inferred from natural user behavior, without requiring explicit calibration routines. The system detects user interactions such as selections of UI elements, and associates them with gaze direction data captured immediately beforehand. Fixation filtering is applied to identify stable gaze intervals based on dispersion thresholds and minimum duration criteria.

The invention belongs to the technical field of intelligent traffic sensor calibration, and particularly relates to an end-to-end thunder all-in-one machine automatic calibration system and method based on deep learning, and the system comprises a data collection module, an image target detection module and a calibration network module. The system collects images and three-dimensional coordinates of a target point through a camera and a radar. Identifying a target in the image by using a deep learning model and acquiring pixel coordinates; and the calibration network learns the mapping relation between the image and the radar point, and directly outputs a high-precision calibration matrix T. According to the method, end-to-end automatic calibration in the whole process is achieved, manual arrangement of calibration plates or selection of corresponding points is not needed, efficiency is remarkably improved, and stable operation can be achieved in complex scenes such as dynamic traffic flow, night and rainy days. Through deep fusion of the high-dimensional features of the Leiyu, error accumulation of step-by-step calibration is effectively avoided, the projection error can be controlled within 5 pixels, and the high-precision sensing requirement of intelligent traffic is met.

The invention discloses a projector calibration method and device, a storage medium and electronic equipment, and relates to the technical field of projection, and the method comprises the steps: sequentially projecting a plurality of pictures of different colors through a projector, and carrying out the color coordinate collection of the plurality of pictures, and obtaining the test color coordinate data of a plurality of colors; performing combination processing on the test color coordinate data of the plurality of colors according to a projection calibration matrix layout to obtain a test polynomial matrix; performing conversion and rearrangement processing on the test polynomial matrix to obtain a test color gamut matrix matched with the specification of the projection processor; and calculating according to the test color gamut matrix and a standard color gamut matrix to obtain a color gamut mapping conversion matrix, so that the projector performs projection based on the color gamut mapping conversion matrix. According to the invention, the color of the projector can be effectively calibrated, the color distortion improvement effect of the projector is improved, and the user experience is improved.

The invention belongs to the technical field of target detection, and particularly relates to a fast target detection algorithm based on multi-modal fusion, which comprises the steps of data acquisition and calibration, synchronous acquisition of images and point clouds and completion of internal and external parameter calibration; performing data enhancement operation such as zooming on the image, performing enhancement such as Pill-processing on the point cloud, and sharing geometric transformation to keep bimodal alignment; obtaining a two-dimensional bounding box and a confidence coefficient by using a YOLOv5 network; obtaining a three-dimensional bounding box by using a PointPillars network, and outputting a projection frame and a confidence coefficient; projecting the three-dimensional bounding box to a pixel plane according to the calibration matrix to form a bounding box; when the intersection-to-union ratio is greater than 0.5, generating a matching pair; each matching pair is coded into a 14-dimensional vector; vectors are input into a multi-layer perceptron, single-mode false detection is effectively inhibited through geometric and semantic consistency screening, 14-dimensional input features learn a nonlinear relation of a multi-mode bounding box through a three-hidden-layer MLP, and fusion precision is remarkably improved.

An automatic end effector tip position initialization mechanism for a micromanipulation system is provided. The method includes: S1, positioning a macro end effector tip, calculating a camera pose by using ArUco Tag, and realizing a three-dimensional positioning of the end effector tip by using a triangulation method, and moving the end effector to a area near a center of a focal plane based on a visual servo; S2, positioning a micro end effector tip; S3, acquiring calibration data based on a visual servo, calibrating the end effector tip and the petri dish, and obtaining a left calibration matrix and a right calibration matrix reflecting the transformation relationship between left and right micromanipulator coordinates and image coordinates; generating an intuitive human-computer interaction interface based on a mouse and a keyboard according to acquired position information. The method has obvious advantages in terms of operation accuracy, operation efficiency and repeatability.

The invention discloses a dynamic environment multi-line radar calibration method and system, and relates to the technical field of road radars, and the system comprises a dynamic reference field generation unit which is disposed on the ground of a radar coverage area, is composed of a reflection target array with known space coordinates, and is adjustable in target distance, and a multi-line laser radar unit which is provided with a horizontal field angle of not less than 120 degrees, and is provided with a multi-line laser radar unit. The number of lines in the vertical direction is larger than or equal to 128 lines per frame, the radar body is installed on the portal frame and connected with the control device, the environmental parameter sensing unit collects the temperature, three-dimensional acceleration and power supply voltage data of the radar body in real time, and the distortion compensation module constructs a space distortion field model based on real-time point cloud feedback and environmental parameters of a dynamic reference field, outputs a calibration matrix and outputs the calibration matrix. And the multi-radar collaborative calibration module is used for synchronizing space-time reference through a PTP protocol. Through a real-time compensation mechanism driven by environmental parameters, vibration acceleration and temperature change data are converted into space coordinate correction, and high-precision stable measurement in a dynamic environment is achieved.

The embodiment of the invention provides a hand-eye calibration method, device and system based on a genetic algorithm. The method comprises the steps that point cloud data collected by a line structure light sensor are acquired for multiple times, and corresponding feature points are determined; determining the coordinate of each feature point in a flange plate coordinate system and the coordinate of each feature point in a sensor coordinate system; determining an initial value of a hand-eye calibration matrix through an ICP algorithm based on the coordinates of the plurality of feature points in the flange plate coordinate system and the coordinates of the plurality of feature points in the sensor coordinate system; and taking the initial value of the hand-eye calibration matrix as a priori solution of a genetic algorithm, and completing the solution of the optimal hand-eye calibration matrix through multiple iterations. According to the method, the calculation efficiency is guaranteed, high-precision global optimization is achieved, then it is ensured that the robot system completes complex operation tasks based on the accurate coordinate conversion relation, and the production quality and efficiency are improved.

The embodiment of the invention provides a phase focusing method, electronic equipment, a storage medium and a program product, and is applied to the technical field of electronics. According to the method, under the condition that an optical anti-shake module is started, a first phase image collected by a phase detection pixel and a first coordinate of the position where the optical anti-shake module is located at a target moment are obtained, and a phase gain compensation matrix is determined according to the first coordinate and a plurality of phase gain calibration matrixes; the relative positions of the optical center of the lens and the optical center of the image sensor are different when the plurality of phase gain calibration matrixes are calibrated, the phase gain compensation matrix is adopted to compensate the first phase image, and phase focusing is carried out based on a second phase image obtained after compensation. Therefore, according to the embodiment of the invention, the brightness value of the pixel in the second phase image obtained after compensation can be more uniform, so that the phase difference information determined based on the second phase image is more accurate, and the focusing stability and the focusing accuracy in an optical anti-shake mode are improved.

The invention relates to a multi-component gas monitoring method. The method comprises the following steps: S100, a processing unit sends a control instruction to control a runner device to switch a filter; s200, after the filter switching is completed, the processing unit sends a calibration matrix obtained through pre-calibration to the imaging detector; s300, the imaging detector calibrates the spectral image data collected in the monitoring area according to the calibration matrix; s400, the processing unit sends the calibrated spectral image data to an upper computer; s500, the upper computer calculates the spectral image data and outputs an infrared video signal to a display device; s600, displaying the infrared video signal by a display device; and S700, returning to the step S100, and continuing to execute the next monitoring cycle. According to the invention, the system operation is simplified, the complexity and time delay caused by switching OCC calibration parameters are avoided, the efficiency and accuracy of multi-gas monitoring are improved, and the method is particularly suitable for real-time monitoring in a dynamic environment and has higher stability and reliability.

The invention relates to the technical field of multi-spectral colorimeter inter-table difference reduction, and discloses a method and device for reducing multi-spectral colorimeter inter-table difference, electronic equipment and a medium, and the method comprises the steps: measuring a set number of color cards to obtain measurement matrixes of different colorimeters, and then selecting one colorimeter from the preset colorimeter group as a standard colorimeter, taking the rest colorimeters as to-be-calibrated colorimeters, and establishing a conversion relation between the standard measurement matrix and the to-be-calibrated matrix. The method has the beneficial effects that the consistency between the to-be-calibrated colorimeter and the standard colorimeter is greatly improved, the calibration procedure is simplified, the reliability and consistency of the calibrated colorimeter in practical application are also improved, the applicability of the multispectral sensor in the corresponding color measurement field is enhanced, and the method is suitable for popularization and application. The cost performance and application universality of the multispectral colorimeter are remarkably improved, and a more stable and accurate color measurement solution is provided for various industries.

The invention provides a string tomato picking robot carpopodium instance segmentation method and system. The method comprises the following steps: constructing a string tomato carpopodium data set and performing enhancement preprocessing; establishing a YOLO11-FFTDA instance segmentation model, performing feature extraction by using a feature pyramid comprising a frequency pyramid attention module in a backbone network, and enhancing frequency domain edge details through fast Fourier transform; a C2PSADA mixed attention module is introduced in the neck network fusion stage, and adaptive sampling points are generated by using a deformable attention mechanism so as to fit the geometrical morphology of the carpopodium and suppress background noise; meanwhile, an independent high-resolution branch is led out to capture a tiny target; and outputting the types, bounding boxes and masks of fruit stems through a decoupling segmentation head, extracting a skeleton center based on the masks to calculate an optimal cutting point, and combining with a hand-eye calibration matrix to realize precise positioning and picking of the mechanical arm. According to the invention, closed-loop control from visual perception to physical operation is realized, and the operation success rate and environmental adaptability of the skewered tomato picking robot are remarkably improved.

The embodiment of the invention discloses a camera and radar external parameter optimization method based on a working site moving target, and the method comprises the steps: collecting a continuous frame image and a point cloud, and extracting an image moving target and a point cloud moving target through an optical flow method and a frame difference method; projecting a moving target to an image by using an existing calibration matrix, performing rigid body transformation matching, and storing a matching result into M; image and point cloud edge features are extracted and obtained through matching; and obtaining an external parameter by using a PNP algorithm, calculating an error, outputting the error if the error reaches the standard, otherwise, rejecting the error and repeating feature matching and subsequent steps. According to the method, iterative optimization of the external parameter matrix is carried out by using the point cloud and image features of the moving target, the moving target with a large error is eliminated, camera and laser radar external parameter calibration based on any scene moving target is realized, the problems that calibration depends on a calibration board and external parameter correction cannot be carried out in the field use process are effectively solved, and the calibration accuracy is improved. And the measurement requirements of the transformer substation can be well met.

The invention belongs to the field of geophysical exploration, and particularly relates to a very-low-frequency three-component coil calibration method based on frequency sweep excitation, which comprises the following steps: acquiring an alternating-current excitation current and a time domain signal of a three-component coil, and performing frequency domain transformation on the time domain signal to obtain a plurality of frequency domain response vectors covering a frequency sweep range; constructing a linear equation set for describing a coil system error based on a plurality of frequency domain response vectors measured under each attitude and the calculated excitation magnetic field reference vector, and solving the linear equation set by using a least square method to obtain a calibration matrix for correcting measurement data; and the calibration matrix is adopted to correct the measurement data of the three-component coil in the use state, so that systematic errors of the very-low-frequency three-component coil can be accurately calibrated and calibrated, and the measurement precision and data reliability of a very-low-frequency electromagnetic method are effectively improved.

Temperature compensation algorithm for water quality conductivity sensor. Using a series of conductivity standard solutions with different concentrations, the conductivity values at 25 °C are measured by a standard conductivity tester calibrated with a calibration solution and recorded as K<subgt;ref< / subgt>. At appropriately spaced temperatures, the conductivity sensor for which a temperature compensation model needs to be established is placed in each of the above standard solutions, and the initial conductivity readings at each temperature and each concentration are measured and recorded as K<subgt;T< / subgt>, that is, the initial conductivity reading of the standard solution at a temperature of T °C. Summarize and form a three-dimensional calibration matrix of water temperature T - initial conductivity reading K<subgt;T< / subgt> - calibration ratio β<subgt;T< / subgt>. Use this to perform temperature compensation on the detected water sample, improving the accuracy of temperature compensation and further ensuring the compensation effect.

The invention provides a chromaticity calibration method, device and equipment based on color classification and brightness segmentation. The method comprises the following steps: acquiring a sensor measurement value of a current display picture of a display through a chromaticity sensor; extracting chromaticity features and brightness features of a current display picture according to the sensor measurement value; determining a color category and a brightness category of the current display picture according to a preset category discrimination strategy based on the chromaticity feature and the brightness feature; determining a calibration matrix of the current display picture according to a pre-stored calibration matrix mapping table based on the color category and the brightness category; and performing linear transformation on the sensor measurement value according to the calibration matrix to obtain a corresponding measurement calibration value. Through color and brightness two-dimensional classification and exclusive calibration matrix adaptation, full-color-gamut and full-brightness refined calibration is realized, the color deviation of the full-color gamut is remarkably reduced, and the color rendition precision and the brightness consistency are improved.

The invention discloses an automatic mounting method and system for a cambered surface wood board based on dynamic point cloud analysis, and the method comprises the steps: building a joint coordinate system of a mechanical arm and point cloud collection equipment, and obtaining a calibration matrix through a specific motion track of the mechanical arm; carrying out initial scanning on the cambered surface substrate on which the wood board is not mounted, and carrying out single-side arc fitting by utilizing a single-side point cloud fitting algorithm to obtain an initial circle center coordinate and a radius; entering a dynamic installation cycle, collecting local point cloud data of the current installation area, and executing Z value mutation feature detection to identify the edge position of the installed part; calculating an actual installation deviation based on the edge position, and dynamically compensating a pose parameter of the wood board to be installed according to the deviation; and according to the cambered surface radius updated in real time, the installation number of the remaining wood boards is adjusted in a self-adaptive mode, and control instructions of the to-be-installed wood boards are generated in batches and sent to the mechanical arm to be installed till all laying is completed. According to the invention, the installation precision and the engineering efficiency of cambered surface packaging are obviously improved.

Provided is an apparatus for assisting driving of a vehicle, the apparatus including: a camera mounted on the vehicle and configured to have a front field of view of the vehicle and acquire image data; a lidar mounted on the vehicle and configured to have a front-side field of view of the vehicle and acquire lidar data; and a controller configured to process at least one of the image data or the lidar data, wherein the controller includes a calibration matrix for correcting the image data and the lidar data and is configured to: obtain a feature map of the image data and a feature map of the lidar data using machine learning; correct the calibration matrix based on a difference between the feature map of the image data and the feature map of the lidar data; and correct the image data and the lidar data using the corrected calibration matrix.

The present application relates to the field of intelligent garbage sorting, and discloses a robot hand-eye calibration method for intelligent garbage sorting, comprising the following steps: Step 1, system initialization, configuration of robot, visual sensor and conveyor belt parameters; Step 2, garbage is transported to the sorting area via a conveyor belt, and the location and category of the garbage are identified by machine vision; Step 3, based on the identification result, the robot controls the metal suction cup to grab the garbage and perform the sorting operation; Step 4, during the sorting process, the target point data of the visual coordinate system and the robot coordinate system are obtained, and the hand-eye calibration matrix is ​​optimized by the H∞ robust control theory; Step 5, the Riemann manifold optimization method is used to perform gradient descent iteration in the manifold space of the rigid body transformation matrix, and by adopting a real-time fault detection technical solution based on sensor networks and data analysis, and by real-time monitoring and fault warning of key components, the technical effect of improving the stability of the garbage sorting system is achieved.