2716 results about "Industrial robotics" patented technology

The invention relates to the technical field of industrial robots, in particular to an industrial robot disordered grabbing system and method based on multi-modal sensing, and the system comprises a multi-modal sensing module, a pose estimation and correction module, a grabbing task planning module and a mechanical arm execution module. The multi-modal sensing module is used for acquiring RGB image data, depth image data, point cloud data and force / torque data in a working environment, and the pose estimation and correction module performs feature extraction and fusion on multi-modal data based on topology invariant mapping and performs adaptive pose estimation on a target object. The grabbing task planning module generates an optimal grabbing strategy based on deep reinforcement learning and outputs a grabbing control instruction, and the mechanical arm execution module receives the grabbing control instruction, controls a mechanical arm to execute a grabbing action and monitors a grabbing state through force / torque feedback. And the environmental adaptability and the perception robustness are improved.

The invention relates to the field of industrial robots, and discloses an industrial robot real-time maintenance system and method in combination with edge computing, and the method comprises the steps: obtaining the multi-source operation state data of an industrial robot, and constructing an operation feature data set of key parts of the robot in combination with a state coding mechanism of an edge end and a feature coupling analysis method; carrying out rapid distributed processing on the operation characteristic data set, constructing an equipment health state model based on a lightweight time sequence modeling algorithm, and introducing a multi-dimensional correlation analysis mechanism to carry out incremental learning on the model; judging whether the edge side state recognition result is stable or not based on the change trend of the trigger frequency; according to the corrected state mapping relation, performing response level division on the potential fault trend by applying a multi-scale fault prediction mechanism, and extracting matched maintenance plan parameters; and based on the maintenance scheduling plan, in combination with a preset fault handling knowledge base, performing automatic evaluation and optimization on the current maintenance strategy. The method has the advantage of improving the response speed.

The invention relates to the technical field of industrial motor faults, in particular to an industrial robot motor fault early warning method and system. The method comprises the following steps: acquiring a motor control instruction of the industrial robot in real time, and then judging an instruction value change event to obtain a motor load change event; monitoring transient working condition characteristic data of the motor in real time according to the motor load change event; flexible gear jump response abnormity evaluation is carried out based on the motor transient state working condition characteristic data, and a flexible gear comprehensive response abnormity score is generated; electromagnetic fault feature analysis is carried out according to the transient working condition feature data of the motor to obtain electromagnetic fault feature coupling data; and according to the electromagnetic fault feature coupling data and the flexible gear comprehensive response abnormal score, performing motor flexible gear health state analysis so as to realize industrial robot motor fault early warning. According to the invention, through working condition jump point detection and motor flexible gear health state analysis, the motor fault can be accurately identified, and multi-stage fault early warning is realized.

The invention provides an industrial robot welding track real-time optimization method and system based on deep learning, and relates to the technical field of industrial robots, and the method comprises the steps: collecting three-dimensional point cloud data, carrying out the multi-scale processing to generate a high-precision curved surface, constructing a welding seam feature descriptor, and carrying out the real-time optimization of the welding track of an industrial robot; and the mapping relation between the technological parameters and the welding posture is optimized on the basis of a multi-target reward function, the mapping relation is converted into an initial movement track, the track is dynamically optimized through a segmented self-adaptive optimization model and a variable structure filtering algorithm, and self-adaptive adjustment of the welding process is achieved. The welding precision and quality of the complex curved surface are improved, and the adaptability and stability of robot welding are enhanced.

The invention provides a self-adaptive teaching-free welding robot intelligent gas saving monitoring system, which relates to the technical field of industrial robots, and comprises a three-dimensional sensing module used for identifying the workpiece material category through a multispectral sensor integrated with a welding gun of a welding robot, meanwhile, a stereoscopic vision camera and a line structure light scanning camera are used for obtaining three-dimensional point cloud data of the welding seam in real time; the parameter adjusting module is used for calling a heat conductivity coefficient database based on the three-dimensional point cloud data, and correcting the initial flow of the welding current, the arc voltage and the protective gas in real time; and the matching control module is used for calculating the arc length variable quantity based on the arc voltage, controlling the opening degree of the gas flow valve through a dynamic regulator, realizing dynamic matching of the gas flow and the changed arc length, and generating gas dynamic matching control parameters. Through cooperative operation of self-adaptive attitude compensation and real-time gas consumption monitoring, teaching-free accurate operation in the welding process is achieved, and intelligent control over gas consumption is improved.

The invention provides a bearing for an industrial robot, and belongs to the technical field of industrial robot bearings. The inner ring is arranged between the inner walls of the outer ring, and a roller path is formed in a gap between the inner ring and the outer ring; a plurality of rollers are arranged, the plurality of rollers are distributed between the inner walls of the roller path in a crossed manner, and spacing blocks are filled among the plurality of rollers; the adjusting mechanism is arranged between the outer ring and the inner ring, the multiple rollers are carried by the multiple spacer blocks to perform central radial movement in the roller path, and the multiple spacer blocks drive the multiple rollers to be uniformly dispersed between the inner walls of the roller path, so that the multiple spacer blocks perform central axial distribution adjustment in the roller path, and the gap positions between the rollers are adjusted; and the rollers are connected to the inner ring and are better attached to the outer ring, so that a roller path is fully filled, the outer ring and the inner ring are radially coaxial, and high-decibel noise generated by collision and friction of the outer ring and the inner ring during high-speed rotation when the outer ring and the inner ring of the bearing rotate at a high speed is avoided.

The invention relates to the technical field of industrial automatic welding and discloses an intelligent control method and system for industrial robot welding, and the method comprises the steps that after a welding task is started, an initial welding process parameter model is called based on the current workpiece specification, and a first welding control instruction sequence is generated; according to the first welding control instruction sequence, a welding gun is controlled to execute welding operation, and multi-source sensing data flow in the welding process is synchronously collected; carrying out fusion feature extraction on the real-time sensing data stream to generate a current welding situation feature vector, when situation deviation exists between the current welding situation feature vector and the reference welding situation feature vector, obtaining a self-adaptive welding process parameter model through online incremental learning, and obtaining a self-adaptive welding process parameter model on the basis of the self-adaptive welding process parameter model. And generating an optimized welding control instruction sequence and issuing the optimized welding control instruction sequence to a robot controller and a welding gun so as to execute self-adaptive welding operation. The welding quality stability and the production efficiency can be improved, and the equipment maintenance cost is reduced.

The invention relates to the technical field of automatic equipment control, in particular to an automatic equipment control system and method and a storage medium. The method comprises the following steps that a high-frequency laser scanner is used for scanning the surface of a target polishing part, B-spline surface fitting is carried out, and a surface fitting surface model is generated; controlling the polishing operation of the polishing industrial robot based on the surface fitting curved surface model, and performing polishing contact state detection to generate contact state point cloud data; based on the contact state point cloud data, grinding expected pose error analysis is carried out, reverse impedance error compensation is carried out, and a reverse impedance compensation amount is obtained; and the joint angle of the polishing industrial robot is corrected through the reverse impedance compensation amount, so that reverse impedance control over the automatic polishing equipment is achieved. By compensating the reverse impedance error of the automatic polishing equipment, accurate control over the automatic polishing process is achieved.

The invention provides a machine grabbing learning method fusing shape features, contact modeling and physical constraints, and the machine grabbing learning method is a robot grabbing posture learning method capable of differentiating. According to the method, a three-dimensional point cloud serves as input, a deep neural network based on sparse voxel convolution is adopted to extract multi-scale shape features, and potential grabbing points and attitude parameters thereof are predicted. The differential contact modeling is realized by constructing a local area of direction perception and extracting geometric features reflecting the contact relationship between the mechanical claw and the object. Furthermore, the collision probability of the grabbing posture is estimated based on an implicit neural network, and a differentiable collision detection module is constructed. Meanwhile, physical constraints such as friction closing, surface alignment and geometric symmetry are introduced to serve as regular terms to jointly optimize grabbing scores, collision risks and stability; according to the method, end-to-end training is supported, efficient, robust and deployable grabbing posture estimation is achieved, and the method is suitable for various automatic grabbing tasks such as industrial robots and service robots.

The invention relates to the technical field of industrial robot path planning, in particular to an industrial mobile robot path planning method based on an artificial potential field method and dynamic prediction. The method comprises the following steps: firstly, acquiring environment point cloud data, and distinguishing static and dynamic obstacles based on time sequence difference and Euclidean distance; kalman filtering is used to predict the future position of a dynamic obstacle, and a density clustering algorithm is used to simplify a static obstacle; combining a target point, a static obstacle set and a dynamic obstacle prediction result to construct an improved artificial potential field function to calculate gravitational force and repulsive force; the potential collision risk is judged based on the motion state of the robot, the dynamic obstacle repulsive force with the risk is brought into potential synthesis in advance, and the final resultant force for guiding motion is generated; and when it is detected that the system is caught in a local optimal or oscillation state, executing a virtual gravity point escape strategy until the system reaches a target point. According to the method, the fusion of static clustering, dynamic prediction and potential field improvement is realized, and the obstacle avoidance and path planning efficiency in a complex dynamic environment is improved.

The invention relates to the technical field of robot control, particularly discloses a path planning and dynamic obstacle avoidance control method for multi-task collaborative operation of an industrial robot, and aims to solve the problems of task scheduling conflict, dynamic obstacle response lag and low collaborative efficiency in a multi-robot system. The method comprises the following steps: constructing a task-resource joint scheduling model and generating initial task allocation; planning a conflict-free collaborative path based on an improved space-time A star algorithm; predicting a dynamic obstacle trajectory by using an LSTM network and generating a space-time envelope; constructing a second-order safety barrier function fusing task priorities; local obstacle avoidance re-planning is realized through rolling horizon model predictive control; and the global rescheduling is triggered when the task delay exceeds the limit or the deadlock risk occurs. According to the technical scheme, closed-loop linkage of global task collaboration and local dynamic obstacle avoidance is realized, and the system collaboration efficiency, the obstacle avoidance success rate and the operation robustness are remarkably improved.

The invention relates to the technical field of industrial robot control, and particularly discloses an intelligent foreign matter sorting robot control system based on AI recognition, which comprises a dynamic spatial feature extraction module, a manipulator motion state coding module, a collaborative conflict detection module, a dynamic trajectory optimization module and an execution control adjustment module, constructing a three-dimensional dynamic space model through multi-sensor fusion, and extracting spatial topological features by utilizing continuous coherence analysis; manipulator motion parameters are converted into topological space representation, and a track feature coding matrix is established; detecting interaction conflicts among the manipulators in real time by adopting a multi-scale coherence analysis method, and generating graded early warning signals; a collision avoidance track is optimized based on topological constraints and a virtual rejection field technology; precise execution is achieved through inverse kinematics of the Lie group theory and self-adaptive control.

The invention belongs to the field of industrial automation, and provides a system based on multi-mode perception and intelligent regulation and control. By integrating various sensors on the robot and applying technologies such as multi-modal sensing fusion, bionics, real-time feedback, multi-source data fusion, reinforcement learning and block chains, accurate grabbing and sorting of food materials, dynamic regulation and control of parameters in the processing process, online quality monitoring, multi-robot collaborative scheduling and quality tracing are achieved. The invention aims to solve the problems of difficult material grabbing, inflexible parameter regulation and control, inaccurate quality monitoring and the like in food processing, improve the automation and intelligence level of the food processing industry, and meet the production requirements of high efficiency, safety and high quality.

The invention relates to the technical field of heavy-load industrial robots, and provides a heavy-load industrial robot path planning and safe and accurate control method, which comprises the following steps: acquiring environment topological data, mechanical arm kinetic parameters and real-time sensing data, performing multi-source information fusion on the environment topological data, the mechanical arm kinetic parameters and the real-time sensing data, and obtaining a path planning result; acquiring a spatial state vector; constructing a target attraction item, an obstacle rejection item, a torque constraint item and an action smoothing item; constructing a reward function according to the spatial state vector, the target attraction item, the obstacle rejection item, the torque constraint item and the action smoothing item; and constructing a target function of model prediction control according to the reward function, and optimizing the joint correction according to the target function. According to the method, multi-source sensor information such as visual sense and force sense can be effectively fused, balance of obstacle avoidance and load efficiency and multi-target collaborative optimization are achieved, and therefore safe and accurate control over heavy-load industrial robot path planning is achieved.

The invention discloses a multi-modal self-adaptive clamping system for assembling key components of a humanoid robot and a control method of the multi-modal self-adaptive clamping system, and the system comprises a multi-modal sensing module which is used for collecting assembling parameters; the edge calculation module is used for generating a control instruction by utilizing a multi-mode deep learning model according to the assembly parameters; the cloud service module is used for generating distillation data according to the assembly parameters; the self-adaptive clamping module is used for clamping and moving key components of the humanoid robot; and the programmable logic control module is used for controlling the self-adaptive clamping module to execute component assembling operation according to the control instruction and the distillation data. According to the invention, a multi-mode self-adaptive clamping system is realized, and the efficiency and the clamping stability are improved. The robot can be widely applied to the technical field of industrial robots.

The invention discloses a digital twin calibration framework construction method based on a Lyapunov strategy, and the method comprises the steps: constructing a digital twin model containing a proxy network to simulate the dynamic state of a system, and defining a state, an action space and a reward function through a Markov decision process. A constrained Lyapunov action-commentator (CLAC) algorithm is introduced, a strategy network and a Lyapunov network are optimized, and the algorithm can stably act under high noise and deviation. Real-time parameter optimization is realized by means of single-time neural network forward propagation, an experience playback pool and the like. Each network structure is clear, and a specific initialization and optimization method is adopted. According to the method, a calibration problem can be converted into a parameter tracking task, efficient training is performed under unmarked data, constraint requirements such as stability can be met, and the method is suitable for scenes such as industrial robot joint control.

The invention relates to the technical field of industrial robot control, and discloses a robot end trajectory optimization method and system based on industrial vision, and the method comprises the steps: obtaining a workpiece surface image in real time through a vision sensor, and obtaining deformation data through image enhancement and feature extraction; when the deformation quantity exceeds a threshold value, calculating a multi-axis coordination parameter by adopting an optimization algorithm to generate a trajectory correction instruction; integrating speed constraints by updating a control model, and determining a final trajectory by using Kalman filtering to fuse sensor feedback data; and the control parameters are iteratively adjusted in the deformation feedback loop, and a stable machining track is formed. The method can solve the problem that in the prior art, the robot tail end track precision is low.

The invention relates to a speed planning optimization method and system for industrial robot repeated path operation, and the method comprises the steps: carrying out the constraint iterative learning speed planning based on path arc length coordinates, building a precise rigid-flexible coupling dynamic model of a robot, and obtaining the motion constraint condition of the robot; calculating the speed planning motion performance of the robot through the motion performance evaluation function, and updating the parameters of the motion performance evaluation function based on a dynamic linear scaling mechanism; an intelligent iterative learning mechanism based on arc length coordinate parameterization, dynamic constraint on-line satisfaction and provable monotonic convergence performance is realized through an updating law of an iterative learning motion performance evaluation function; and outputting the optimal speed curve of the robot. Track deviation, vibration data and energy consumption characteristics are automatically recorded when a task is executed each time, the root of a problem is autonomously analyzed, key parameters such as an acceleration curve and joint torque distribution are dynamically adjusted, and dependence on manual parameter adjustment in repeated operation is eliminated.

The invention discloses an industrial robot image processing method based on image fusion, and relates to the technical field of intelligent aquaculture, and the method comprises the steps: collecting an original image in real time through deploying an image collection device integrating visible light, polarization and multispectral imaging, and extracting suspended matter density, water body light transmittance and illumination intensity change information; generating a first image set; suppressing suspension interference through image filtering and enhancement processing to obtain a first corrected image; extracting an aquatic product individual region, performing multi-source image fusion, identifying color deviation, texture interruption and reflection feature anomaly regions, and constructing a lesion candidate set; gray scale reconstruction, edge gradient and brightness normalization correction of a multispectral channel are executed based on illumination and reflection changes, and a high-quality fusion image is generated; and calculating a health anomaly probability coefficient of the target individual by using the depth recognition model, comparing the health anomaly probability coefficient with a threshold value, and recording a recognition result and collecting information if the threshold value is exceeded. The method can significantly improve the accuracy of aquatic individual lesion recognition.

The invention discloses an industrial robot predictive maintenance method and system based on multi-source data fusion, and the method comprises the steps: synchronously collecting vibration, current, temperature, acoustic and visual signals through multiple types of sensors, carrying out the filtering, correction and normalization processing, and constructing a multi-modal feature set; cross-modal alignment is realized through time compensation, after dimensionality reduction, a mechanical vibration group, an electrical performance group, a thermodynamic group and a motion precision group are divided, mahalanobis distances of the groups are calculated based on a historical health reference to serve as local anomaly degree scores, weights are dynamically adjusted according to the change rate, and the weights are combined into a preliminary health index. And introducing a nonlinear amplification mechanism to enhance high-value response, adaptively switching smooth intensity according to a degradation trend, and outputting a comprehensive health index. According to the method, comprehensive perception and dynamic evaluation of the operation state of the practical training platform are realized, multi-source heterogeneous information is effectively fused, the limitation of single signal monitoring is overcome, and the anomaly recognition accuracy is remarkably improved.

The invention discloses a robot grinding surface roughness prediction method considering dynamic factors based on deep learning, and relates to the technical field of industrial robots. The method comprises the following steps: carrying out self-extraction on grinding machining dynamic factor spatial features by adopting a convolutional neural network; performing time sequence feature extraction on the extracted space by adopting a bidirectional long-short-term memory network; carrying out standardization and normalization processing on the extracted space and time sequence features and static factors; utilizing an improved whale algorithm to optimize hyper-parameters of the bidirectional long-short-term memory network, and further introducing an attention mechanism to realize feature automatic weight distribution; the steps are integrated, and an IWOA-CNN-BiLSTM-Attention surface roughness prediction model is constructed; and inputting the static factors, the extracted features and the surface roughness measurement value into a prediction model for model training, and outputting a surface roughness prediction value to realize a surface roughness prediction function. The method can solve the problem of difficulty in dynamic factor feature selection, and improves the model prediction precision.

The invention relates to the technical field of industrial robots, and discloses industrial robot equipment for clamping and transferring parts, a moving frame is connected with an outer ring seat through a moving assembly, the inner side of the outer ring seat is rotatably connected with a rotating cylinder, and the outer side of the upper end of the rotating cylinder is provided with an outer gear ring located at the top of the outer ring seat; a lifting frame assembly is slidably connected to the inner wall of the rotary drum through a limiting sliding connection assembly, and clamping arms are movably connected to the lifting frame assembly at equal intervals. According to the clamping arm, flexible clamping is achieved through air bag expansion, damage to the surfaces of parts due to rigid contact is avoided, and the clamping arm is particularly suitable for precise parts. By means of the clamping device, the parts can be clamped, and meanwhile the clamped parts can be lifted upwards, so that follow-up position transferring and direction adjusting are facilitated; and high-pressure air is continuously sprayed out in the clamping process, impurities on the surfaces of the parts are removed, and the subsequent machining quality is improved.

The invention relates to the technical field of robot control, and discloses an industrial robot adaptive control method and system based on multi-modal sensor fusion, and the method comprises the steps: collecting multi-modal original data, and carrying out the time-space alignment; capturing space-time semantic association of visual textures, tactile pressure distribution and force sense fluctuation in the multi-modal data through a multi-head attention mechanism guided by a physical model, and performing space-time registration; a CNN-LSTM hybrid model is adopted to extract visual texture features and time sequence tactile features in the physical information enhanced multi-modal feature matrix; and carrying out dynamic weight distribution on the fusion feature vectors with physical consistency by utilizing a weight distribution model driven by element reinforcement learning to generate dynamic weighted fusion features. According to the method, the spatial positioning precision of the industrial robot in a precise assembly scene is greatly improved, the contact force control stability is greatly improved, and the control robustness in a complex operation scene is remarkably enhanced.

The invention provides a brushless motor multi-axis cooperative control method and system for an industrial robot, and relates to the technical field of control, and the method comprises the steps: processing the real-time parameters of a brushless motor of each joint axis through constructing an extended dynamic model, and obtaining a temperature drift coefficient and a load inertia parameter; a self-adaptive pre-compensation model, a neural network compensator and a recurrent neural network prediction model are combined, a torque control instruction is output, a three-phase current prediction value is calculated, a distributed model prediction controller is adopted to generate an optimal switching sequence, multi-axis cooperative control is achieved, and the system stability and the response speed can be effectively improved.

According to the embodiment of the invention, the industrial robot technology is applied to the rolling forming process of the variable cross-section part, and the rolling path can be dynamically adjusted according to the variable cross-section characteristics of the part, so that efficient machining is realized. According to the technology, the limitation of an existing technology in complex geometrical shape machining is broken through and solved, and an industrial robot can conduct high-quality and high-precision rolling forming on the variable cross-section part of a complex structure under the condition that the industrial robot does not depend on a mold. In addition, by combining the flexible path control capability of a robot, the forming precision of the variable cross-section part is remarkably improved, and compared with a raw material, the mechanical property loss of a bending area material of the part obtained after forming through the technology is smaller than 30%.

The invention relates to the technical field of industrial robot control, and discloses a robot-positioner PLC cooperative communication control method and system.The method comprises the steps that an initial cooperative trajectory entropy model is built by receiving a robot tail end pose sequence and a positioner target angle sequence, and the actual pose, servo feedback signals and welding energy flux density parameters are collected in real time; generating a time-varying weight matrix and compensation intensity, performing dynamic optimization and fault-tolerant correction on the trajectory based on the initial collaborative trajectory entropy model, and outputting a collaborative control instruction; the problems of communication delay, limited positioning precision, poor system compatibility, weak fault-tolerant capability and the like in a traditional control method are effectively solved, high-precision synchronous control over the welding robot system and the positioner is achieved, and the production efficiency and the welding quality of the flexible welding workstation are remarkably improved.

The invention belongs to the related technical field of industrial robots, and discloses a control method and system of a crawler-type 3D printing robot capable of autonomously and movably printing. The method comprises the following steps that the current position and the target position of the 3D printing robot are obtained, the current motion path of the 3D printing robot moving from the current position to the target position is planned, and the 3D printing robot moves to the target position according to the planned motion path; acquiring a real-time relative pose between the head of the 3D printing robot and a preset marker, comparing the real-time relative pose with a preset relative pose, and if the real-time relative pose is inconsistent with the preset relative pose, planning a pose adjustment path of the head and adjusting the pose of the head according to the path until the real-time relative pose is consistent with the preset relative pose; and the 3D printing robot carries out printing at the current position and posture. By means of the 3D printing robot, the problem that the 3D printing robot cannot move autonomously and adjust the pose in a self-adaptive mode is solved.

The invention relates to the technical field of industrial robots, in particular to a five-degree-of-freedom industrial robot path planning method and system based on D-H parameters and dynamics modeling, and realizes high-precision path planning and dynamic environment adaptation by combining kinematics modeling, dynamics analysis and MATLAB simulation technologies. The method comprises the steps of building a robot kinematics model, building a kinetic equation, mixing a path planning strategy, solving inverse kinematics and performing multi-sensor fusion control. The system covers modeling, calculation, planning, simulation and feedback modules, and the operation efficiency and safety of the industrial robot in a complex scene are effectively improved.

The invention provides a teaching-free welding robot dynamic energy-saving path planning method and system, and relates to the technical field of industrial robos.The method comprises the steps that 1, a workpiece is scanned through a laser vision device, weld joint coordinates, obstacle information and workpiece thermal deformation data are obtained, weld joint feature points are extracted, and the weld joint feature points are obtained; establishing a mapping relation between a workpiece coordinate system and a robot base coordinate system; step 2, based on the mapping relationship, in combination with a thermal deformation prediction mechanism and sensor feedback data, implanting a dynamic response node in the initial path, calculating path point offset by using the feedback data, and generating an anti-offset path; and step 3, aiming at the anti-offset path, constructing a multi-dimensional collaborative evaluation mechanism, and carrying out synchronous adjustment and comprehensive balance on the path length, the energy consumption of the whole welding process and the welding quality parameters so as to obtain a multi-dimensional collaborative evaluation result. Weld joint deformation, clamp offset and obstacle displacement are sensed in real time, the path is dynamically planned, and the flexibility of path planning is improved.

The invention relates to an ultralow-delay visual positioning system and method based on an event camera. The system comprises an event sensing module, an inertial measurement unit (IMU) and a mixed signal processing unit. The event sensing module has a microsecond-level response characteristic and is integrated with a dynamic bias adjustment and tunable optical filter; the IMU is rigidly connected with the IMU, and the physical center alignment error is less than 0.5 degree; and the hybrid processing unit adopts an FPGA (Field Programmable Gate Array) and ARM (Advanced RISC Machines) collaborative architecture to realize event filtering, time synchronization, motion compensation and tight coupling optimization. According to the method, an event brightness residual error and an IMU pre-integration residual error are combined to construct a joint cost function, a sliding window is dynamically adjusted based on event entropy, and loop-back and closed-loop correction is realized through mutual information criteria. The method can realize six-degree-of-freedom pose estimation as high as 500Hz, has the advantages of low delay, high precision, high robustness and the like, and is suitable for high-speed scenes such as unmanned aerial vehicles, AR navigation, industrial robots and the like.