86 results about "Laser data" patented technology

The invention discloses a high-precision laser positioning unpacking path planning system, and belongs to the technical field of robot automatic control and industrial automation. The system comprises a data synchronization module used for multi-sensor hardware synchronization and data alignment; the high-precision positioning module is used for outputting a precise pose based on environment skeleton characteristics and sliding window optimization; the semantic map construction module is used for fusing vision and laser data to generate a dynamic semantic grid map; the global path planning module is used for planning a smooth path in the skeleton channel by utilizing a mixed potential field improved A * algorithm; the motion control module is used for realizing closed-loop motion control and safety monitoring through model prediction and tracking; and the operation execution module is used for finishing millimeter-level precise stopping of an operation point by adopting visual servo and triggering unpacking operation. According to the method, the positioning robustness under dynamic shielding is improved through the environmental skeleton features, the safety and the high efficiency of the path are ensured by utilizing semantic understanding and intelligent planning, and the full-process automation from navigation to precise operation is realized.

The invention relates to the technical field of laser data transmission, and provides a multi-dispersion laser system and a control method thereof. The control method of the multi-dispersion laser system comprises the following steps: establishing a first model and a second model; in the initial state, the pumping power of the pumping light source is adjusted, and a first target pulse interval and first target power are determined in combination with the first attenuation coefficient, the second attenuation coefficient and the first model; determining a second target power, a second target pulse interval and a target attenuation coefficient by a second model in combination with the fact that the second attenuation coefficient is zero; the real-time pumping power is controlled to be first target power, the first real-time attenuation coefficient is zero, the second real-time attenuation coefficient is zero, the first laser is output in a first state, and the second laser is output in a third state; the third state comprises a soliton dissipation state; the real-time pumping power is controlled to be second target power, the first real-time attenuation coefficient is the target attenuation coefficient, the second real-time attenuation coefficient is zero, the first laser is output in the first state, and the second laser is output in the second state.

The invention provides an indoor robot target detection method and system based on multi-modal tracking and a medium, and belongs to the field of robotics.The method comprises the steps that laser data processing is optimized through static furniture learning and filtering and dynamic region clustering, and the visual anti-illumination interference capacity is combined; a dynamic cost matrix matching strategy based on target motion characteristics is adopted to obtain a stable trajectory of a target person; kalman filtering trajectory prediction and target appearance feature matching are fused, and it is ensured that tracking can be rapidly recovered after the target is temporarily lost; a human-guided following mapping mode is adopted, a moving track of a tracked target is used as dynamic guidance, dynamic and static point clouds are separated, and a high-precision environment map with semantic tags is constructed; according to the invention, a following state monitoring and active service linkage mechanism is established, scene decision is carried out by identifying a target attitude and combining a semantic map, corresponding health monitoring and safety services are triggered, and the crossing from passive tracking to active intelligent services is realized.

The invention relates to a sensor cleaning control method of an unmanned vehicle and a related device thereof, a sensor of the unmanned vehicle is provided with a pollution monitoring unit, and the method comprises the following steps: obtaining multi-element pollution data collected by the pollution monitoring unit; wherein the pollution detection unit at least comprises an image acquisition module and a laser detection module; the multi-element pollution data comprises image data acquired by the image acquisition module and laser data acquired by the laser detection module; the image data and the laser data are analyzed to obtain a pollution result of the sensor, and the pollution result is used for indicating the pollution degree of the sensor; when the pollution result indicates that the sensor is in a pollution state, determining a cleaning strategy based on the multi-element pollution data; and controlling a cleaning system of the unmanned vehicle to clean the sensor according to the cleaning strategy. According to the scheme provided by the invention, the pollution type and degree of the sensor can be accurately identified, a targeted cleaning strategy is adopted, and the cleaning efficiency and the cleaning capability are improved.

The invention discloses a shield tunneling machine attitude deviation measurement method based on laser-vision deep learning heterogeneous fusion, and the method comprises the steps: employing double sensors of a laser target and a vision camera, carrying out the deployment of the sensors, and collecting data, which comprises laser data and vision data; preprocessing the data, wherein the preprocessing comprises laser data preprocessing and visual data preprocessing; the laser data preprocessing comprises laser data coordinate conversion, laser target attitude deviation calculation and noise filtering; the visual data preprocessing comprises image preprocessing, mark point detection, camera calibration and world coordinate calculation, and visual measurement attitude deviation calculation; a deep learning heterogeneous fusion model is constructed, the deep learning heterogeneous fusion model comprises a laser absolute feature branch, a visual spatial-temporal feature branch, an attention fusion layer and an output layer, and the model is trained; and inputting the preprocessed real-time data into the trained deep learning heterogeneous fusion model, and carrying out dynamic calibration on an output result.

The invention belongs to the technical field of welding robots, and particularly relates to a welding robot error correction method based on vision and laser data fusion. The method comprises the following steps: firstly, calibrating parameters on a dual-focus conjugate plane through a binocular camera, establishing a three-dimensional distortion correction model, constructing depth information in combination with a binocular triangle principle, completing laser light plane parameter calibration through an SVD (Singular Value Decomposition) method, and outputting large-field-of-view and high-depth-of-field laser-assisted vision measurement data; secondly, designing a three-dimensional target containing sphere characteristics, initially calibrating hand-eye parameters based on a fixed-point variable attitude method, establishing a hand-eye relationship and distance error identification model, and solving errors to realize robot-vision integrated online calibration; and finally, extracting a welding seam center point in a layered manner, planning a track through unit quaternion interpolation and a mixed curve, and writing an interface to output a welding control scheme. The problems that a traditional method is low in calibration precision and difficult in light bar extraction are solved, the measurement and positioning precision of the welding robot is greatly improved, and the high-precision welding requirement of a large component is met.

The application provides a laser curtain wall image optimization system and method based on laser ranging analysis, and relates to the technical field of laser curtain walls.The application generates snowflake point probability and non-snowflake point probability by performing probability analysis on a snowflake point set and a non-snowflake point set, improves precision through variance analysis, and introduces a variance analysis module for snowflake points and non-snowflake points, which helps to deeply understand the intensity distribution characteristics of different reflection sources, outputs a reflection intensity threshold based on the size of the variance, optimizes the precision of laser ranging, and effectively identifies laser points that have passed through snowflake reflection and laser points that have not passed through snowflake reflection through the setting of a laser point cloud collection module and a division module, thereby realizing accurate classification of laser data; considering the possible jitter of the laser equipment under the influence of wind, the jitter data of the equipment are acquired in real time through a jitter collection module and an analysis module, and correlation analysis is performed with the reflection intensity threshold, thereby ensuring the authenticity of the data.

The invention belongs to the field of robot control, and provides a robot path planning method and system based on a target attention point, and the method comprises the steps: carrying out the environment field modeling based on a Gaussian process according to laser data obtained by a robot; based on a leading edge detection and clustering algorithm, generating target attention candidate points at known-unknown boundaries of an environment field, and taking prediction variances at the candidate points as measurement of field uncertainty; constructing a utility function fusing map information gain, field information gain and path cost, and selecting a target attention point from the candidate points by maximizing the utility function; and generating an optimal path by using a TEB path planner. According to the method, autonomous sampling of the robot can be effectively guided, information acquisition is maximized while the advancing path is optimized, and the precision and efficiency of environment field reconstruction are remarkably improved.

This application discloses a semantic map construction method, apparatus, electronic device, and storage medium. The method includes: acquiring source data for the semantic map, including image data, laser data, inertial navigation data, and satellite positioning data; determining the vehicle pose corresponding to the image data based on the image data and inertial navigation data, and determining the vehicle pose corresponding to the laser data based on the laser data and inertial navigation data; constructing a first nonlinear optimization model based on the vehicle pose corresponding to the image data, the vehicle pose corresponding to the laser data, and the satellite positioning data to determine the optimized vehicle pose; determining the three-dimensional semantic information corresponding to the image data based on the image data and laser data; and constructing a semantic map based on the optimized vehicle pose and the three-dimensional semantic information. This application optimizes the vehicle pose by combining data from multiple sensors, improving the accuracy of semantic map construction, and improves the efficiency of semantic map construction by combining image data and laser data.

The invention discloses a display system based on laser point cloud calibration, the display system comprises a display interface and a controller, and the display interface comprises a point cloud display area and a statistical display area; the controller is used for executing the following steps: step 1, controlling the laser data to be displayed as a real-time point set in the point cloud display area; a first algorithm module controls a target frame to be displayed in a point cloud display area and frames a to-be-calibrated point set; 2, processing the to-be-calibrated point set into a template point set by a second algorithm module, displaying the template point set in a point cloud display area, and removing miscellaneous points from the template point set in an interface interaction mode to obtain an optimized template point set; 3, a third algorithm module controls the optimization template point set to be matched with the real-time point set, and poses of matching points are generated and displayed in a statistical display area; and 4, calculating a statistical evaluation score corresponding to the matching point by a fourth algorithm module, evaluating the stability of the laser point cloud, and displaying the statistical evaluation score in a statistical display area.

The invention discloses a laser detection device capable of continuously measuring the wall thickness of a pipe, and relates to the technical field of laser measurement, the laser detection device comprises a measurement frame, the inner wall of the measurement frame is slidably connected with two mechanical clamping rods, a laser measurement plate and a brush plate are arranged above the measurement frame, and the inner wall of the measurement frame is also provided with a data storage element. According to the device, firstly, through the arrangement of the brush plate, circumferential cleaning work can be conducted on a pipe end opening, chipping attachments generated by cutting on the pipe end opening are removed, the accuracy of follow-up data collection of the laser measuring plate is conveniently maintained, meanwhile, the brush plate has the characteristic of moving in advance, and the practicability of the device is improved. The pipe wall thickness data collected by the laser measuring plate on a detection path can be ensured to be accurate data after cleaning, the brush plate and the laser measuring plate can keep a certain distance, the brush plate and the laser measuring plate do not interfere with each other, and in the rotating process of the laser measuring plate, the phenomena of chipping falling and the like do not occur, so that the pipe wall thickness can be accurately measured. And the accuracy of laser data recording is influenced.

The application discloses a kind of multi-source asynchronous laser scanning data fusion and three-dimensional imaging method and system based on heterogeneous embedded system, the method is for the time dislocation between high-frequency laser data and low-frequency position feedback, through Kalman filtering algorithm, realize the motion state smooth redrawing of microsecond level;Using the hardware parallelism of FPGA, the PL end synchronous sampling ADC data and encoder position are implemented nanosecond level timing alignment;In three-dimensional space projection stage, introduce adaptive weight fusion based on space topological relation, solved the artifact distortion problem when point cloud is projected to grid;DMA interrupt driven ping-pong buffer mechanism ensures that ADC data acquisition and PS end processing are full-pipeline parallel, system throughput is not affected by CPU processing delay.The application effectively solves the data fusion distortion problem caused by heterogeneous multi-source data sampling rate mismatch and timing asynchrony, improves the continuity, real-time and spatial resolution of laser scanning imaging.

This invention discloses a cliff detection method and terminal. Single-point lasers are installed downwards directly in front of and behind the left and right drive wheels of a vehicle, respectively. A single-point laser is also installed at an angle downwards directly in front of the vehicle. The cliff situation near the drive wheels is measured using these single-point lasers. The angled single-point laser at the front of the vehicle avoids the laser detection position being too close to the front wheels when the vehicle is moving, preventing the vehicle from failing to stop in time due to inertia. The method selects the appropriate single-point laser group for detection based on the vehicle's motion state. Therefore, irrelevant single-point laser data does not participate in the logic under different motion states, reducing the possibility of false signal triggering. If the distance detected by one of the single-point lasers in the single-point laser group exceeds a preset distance, the vehicle braking program is triggered, and the vehicle moves in the direction of the single-point laser whose detected distance is within the preset distance. This accurately assesses the surrounding ground environment and improves the accuracy of cliff detection.

The invention relates to a guide rail support spacing adjusting device and method for an elevator, and belongs to the technical field of elevator installation and maintenance, and the guide rail support spacing adjusting device comprises a measuring trolley climbing along a guide rail, a short-range laser sensor array driven by an actuator, a foresight distance sensor and a tilt angle sensor; laser, stroke and attitude data are synchronously collected during climbing; detecting the guide rail bracket by a foresight sensor; when the guide rail bracket is detected, recording an initial position and withdrawing the laser array; after crossing the bracket, recording a termination position and recovering the array; correcting the laser data according to the attitude data, and interpolating and reconstructing a guide rail contour of a support shielding area to generate an adjusting scheme; coevolution of measurement and avoidance is achieved, the existence of the foresight distance sensor provides a pre-judgment capability for a control algorithm, the avoidance action can be carried out actively in advance, and the method is a physical basis for achieving intellectualization of the algorithm.

The application relates to the technical field of target detection, and discloses a detection method and device for small targets on a road during automatic driving, which comprises the following steps: obtaining original laser data of an intelligent automobile during driving by using a laser module, and shooting original shooting pictures during driving by using a camera module; performing first target detection on the original shooting pictures to obtain a first target detection frame; obtaining a target to-be-corrected point set by taking each pixel point in the first target detection frame as a target to-be-corrected point; converting original laser data belonging to a laser coordinate system into a focal length coordinate system by using a camera coordinate system to obtain focal length laser data; cleaning the target to-be-corrected point set by using the focal length laser data to obtain a corrected detection frame; and replacing the first target detection frame with the corrected detection frame to obtain a target detection result during automatic driving of the intelligent automobile. The main purpose of the application is to solve the problem that a target detection frame generated by detecting small targets during automatic driving is inaccurate.

This invention discloses a dynamic environment SLAM localization method based on multi-source sensor data fusion, comprising the following steps: acquiring visual, inertial, and laser data and calibrating the buffer; performing HRNet segmentation and ORB-SLAM tracking on the visual data to obtain the initial pose; fusing the inertial and laser results to obtain the current pose; determining and recalculating the divergence of time window data to update the current pose; inserting keyframes and generating a semantic skeleton map; encoding and generating a semantic topology string and associating it with the keyframe index; retrieving loop closure candidate frames and performing geometric verification followed by map optimization to obtain the localization result and map result. This invention achieves highly robust localization and mapping in dynamic environments and improves the accuracy of loop closure recognition and map consistency.

This invention provides a 3D laser SLAM fusion magnetic stripe inspection robot navigation and positioning method, belonging to the field of navigation and positioning technology. The method includes: initializing the robot by acquiring an initial pose and presetting a rectangular boundary for a dynamically changing region; determining in real time whether the current pose is within the dynamically changing region; if the current pose is outside the dynamically changing region, registering the current frame of laser data with a pre-constructed point cloud map to obtain a first positioning pose; if the current pose is within the dynamically changing region, performing laser fusion magnetic stripe positioning: acquiring the initial 3D positioning pose and initial magnetic stripe data; determining the initial vertical deviation distance; acquiring the real-time 3D positioning pose and real-time magnetic stripe data; determining the real-time vertical deviation distance; and correcting the pose output by the laser odometry through forced constraints to determine the final fused pose. According to this invention, interference in dynamically changing regions can be better addressed, achieving stable and reliable navigation and positioning.

This application discloses a map building optimization method, apparatus, electronic device, and readable storage medium, applied in the field of data processing technology. The map building optimization method includes: acquiring at least two unit maps generated by a preset mapping algorithm based on laser data; performing edge optimization on each unit map to obtain an optimized unit map; processing each optimized unit map based on the overlapping areas between each pair of optimized unit maps to obtain a processed unit map; and combining the processed unit maps into a target map. This application solves the technical problem of inaccurate map building.

The invention discloses a laser welding seam detection method and system combined with image recognition, and relates to the technical field of image recognition. A plurality of pit areas are determined based on image recognition of a welding seam three-dimensional image; determining a plurality of welding seam control nodes according to the pit space of each pit area, the corresponding surface morphology and the area variation of the two adjacent pit areas, and determining a corresponding welding seam quality event based on dynamic detection of each welding seam control node. According to the welding seam quality event of each welding seam control node, the corresponding node position and the laser data combination of the laser welding head, the corresponding laser welding seam detection system is determined, and the accuracy of the laser welding seam detection system is improved. And corresponding welding data and dynamic working data are determined based on detection of the laser welding head, and a welding state curve of the laser welding head is determined according to the welding data and the dynamic working data of the laser welding head and the quality grade of each key welding line area.

This invention discloses a tunnel deformation monitoring device based on millimeter-wave radar, comprising a main housing, millimeter-wave radar, laser sensor, PCBA, and bubble level. It integrates multimodal data fusion analysis, dynamic measurement range intelligent adjustment, real-time deformation prediction and control, self-calibration, and fault diagnosis modules. The multimodal fusion system fuses radar and laser data using a Kalman filter algorithm, and utilizes median filtering, Gaussian fitting preprocessing, and dynamic weight allocation to achieve high-precision monitoring across the entire measurement range. The dynamic measurement range module, based on a decision tree algorithm, intelligently switches the measurement and frequency sweep range according to mining progress, geological conditions, and distance fluctuation coefficient. The real-time prediction module uses an ARIMA model to predict deformation in advance and automatically adjusts the scanning angle and acquisition frequency. The self-calibration module corrects errors daily using a high-precision reflector. Supporting 5G communication and remote control, it combines high precision, adaptability, and high reliability, making it suitable for long-term deformation monitoring of mine tunnels.

The application provides a self-rescue method and robot for a virtual expansion layer of an obstacle, a storage medium, and the method comprises the following steps: if it is identified that the robot contour enters the expansion layer, a global planning path and a current pose are acquired; the closest path point in the global planning path which is at least separated from the robot by a first preset distance and has not been run to is calculated; if it is determined according to laser data that there is no obstacle in the front of the robot, the closest path point is taken as a running target point; if there is an obstacle, a point position meeting preset requirements is taken as a running target point; the preset requirements comprise that the included angle with the current pose is within a first preset angle range, and there is no obstacle in the range after the left side and the right side are respectively expanded by a second preset angle. A feasible self-rescue strategy is formulated according to the environment to safely escape, human intervention is reduced, and the original safety strategy does not need to be changed, so that the running safety is ensured.

The invention relates to the technical field of laser ranging calibration, and discloses a high-precision laser ranging calibration method and system. The system comprises a laser data acquisition module, a noise filtering processing module, an environment compensation module, a dynamic calibration module, a precision verification module, a re-calibration triggering module, a data integration module, a safety verification module, a report generation module and a closed-loop feedback module. When high-precision laser ranging is carried out, by sensing and compensating influences of environmental parameter changes of temperature, humidity and atmospheric pressure on the laser propagation speed in real time, ranging systematic deviation caused by environmental fluctuation can be eliminated, accuracy and reliability of ranging results under different working conditions are guaranteed, environmental adaptability of a ranging system is improved, and the range finding precision is improved. When laser ranging signals are processed, the advanced signal decomposition and threshold filtering technology is adopted, signal noise introduced by equipment and external interference can be restrained, and ranging waveform distortion and feature extraction errors are avoided.

This invention belongs to the field of tunnel detection technology and provides a method and system for identifying tunnel structural defects based on multispectral sensing and SLAM. Based on multiple response timestamp data, a residual squared sum objective function is constructed to represent the difference between the theoretically predicted clock vector and the actual measured timestamp matrix. Through iterative optimization, the optimal smooth estimate of the clock drift rate is determined, reducing the combined effects of electromagnetic interference and temperature / humidity variations. A three-dimensional point cloud map is constructed based on the coupling of axial integrals of inertial data and radial constraints of laser data, achieving long-distance, high-precision passive positioning under tunnel corridor effects. Furthermore, a lightweight identification model using a multidimensional knowledge distillation architecture and geometrically guided fraction fusion reduces edge memory consumption. By combining the reduction of electromagnetic interference and temperature / humidity variations, overcoming positioning drift under the dual constraints of corridor effects, and breaking through edge computing power limitations, the tunnel defect identification effect is improved.

Example methods and systems for detecting weather conditions using vehicle onboard sensors are provided. An example method includes receiving laser data collected for an environment of a vehicle, and the laser data includes a plurality of laser data points. The method also includes associating, by a computing device, laser data points of the plurality of laser data points with one or more objects in the environment, and determining given laser data points of the plurality of laser data points that are unassociated with the one or more objects in the environment as being representative of an untracked object. The method also includes based on one or more untracked objects being determined, identifying by the computing device an indication of a weather condition of the environment.

This invention discloses a mapping method and system based on the fusion of single-line LiDAR and depth camera, belonging to the field of SLAM mapping technology. The method includes the following steps: S1: joint calibration; S2: ORB descriptor processing; S3: pseudo-LiDAR data conversion; S4: mapping completion. This invention ensures data consistency in time and space dimensions through joint calibration, improves the accuracy and efficiency of feature extraction and matching through ORB descriptor processing, and achieves effective fusion of the two types of data through pseudo-LiDAR data conversion. The resulting 2D grid map, integrated with the Cartographer algorithm framework, significantly improves obstacle detection rate, provides clearer details, accurately displays obstacle locations, avoids collisions during robot navigation, and effectively suppresses map drift. This greatly improves the accuracy and robustness of the SLAM system, enhances the robot's obstacle avoidance ability in complex environments, and reduces costs compared to 3D LiDAR, making it of significant practical application value.

The application provides a double-curvature fusion curved surface SLAM navigation method, a longitudinal and transverse double-direction curvature parameter is estimated in real time through double-IMU fusion, an extended information filtering framework containing a double-curvature kinematic model, a laser data curved surface projection compensation algorithm and a path double-curvature compensation algorithm are constructed, and the scheme can accurately depict the curved surface geometric constraint where the robot actually locates; the accumulation of the transverse positioning error is inhibited through online estimation and fusion of the transverse curvature parameter; the accurate description and compensation of the robot side slip effect are realized through the establishment of a kinematic model containing a double-curvature coupling term; the data distortion is eliminated and the environment perception accuracy is improved through the projection of the laser scanning data from a plane to the estimated curved surface; finally, the consistency of the control instruction and the curved surface geometry is ensured through the real-time geometric compensation of the planning path based on the double-curvature parameter, and the stability and trajectory tracking accuracy of the control system under the longitudinal and transverse curvature coupling effect are enhanced.

The application relates to the technical field of laser data transmission, and provides a multi-dispersion laser system and a control method thereof. The control method of the multi-dispersion laser system comprises the following steps: establishing a first model and a second model; in an initial state, adjusting a pumping power of a pumping light source, combining a first attenuation coefficient, a second attenuation coefficient and the first model to determine a first target pulse interval and a first target power; combining the second attenuation coefficient being zero and the second model to determine a second target power, a second target pulse interval and a target attenuation coefficient; controlling the real-time pumping power to be the first target power, the first real-time attenuation coefficient to be zero, the second real-time attenuation coefficient to be zero, the first laser to be in a first state and the second laser to be in a third state; the third state comprises a dissipative soliton state; controlling the real-time pumping power to be the second target power, the first real-time attenuation coefficient to be the target attenuation coefficient, the second real-time attenuation coefficient to be zero, the first laser to be in the first state and the second laser to be in a second state.

The invention provides a laser detector for measuring the track gauge and coplanarity of an elevator guide rail. The laser detector comprises a laser data collector and a target, the laser distance measuring sensor is arranged at the front part of the laser data acquisition unit, and the detection end of the laser distance measuring sensor detects in the direction deviating from the laser data acquisition unit; the power switch and the charging interface are located on the right side of the laser data collector; the control circuit board is arranged in the lower half area of the interior of the laser data collector and electrically connected with the laser distance measuring sensor, the power switch, the key, the screen and the Bluetooth module; the whole target is in a right-angle state, and the back of the target is provided with small magnetic steel like the laser data collector; the elevator guide rail gauge and coplanarity laser detector can measure whether the guide rails of an elevator are on the same plane or not and measure the distance and deviation between the guide rails. The measuring instrument can measure the track gauge and coplanarity of the elevator guide rail.

The application provides a low-cost portable indoor three-dimensional mapping method and device, which performs multi-sensor data synchronous acquisition, comprises setting two linear scanning laser sensors with different axial positions, a vertical downward single-point laser ranging module and a MEMS inertial measurement unit, simultaneously performing data acquisition on the state and indoor space; sensor space relative relationship calibration is performed to determine the fusion of horizontal linear scanning laser data and the MEMS inertial measurement unit in two-dimensional position and posture; two-dimensional position and posture and laser ranging joint three-dimensional position and posture calculation comprises the following steps: performing optimal estimation on three-dimensional position and posture by utilizing extended Kalman filtering to fuse two-dimensional position and posture, MEMS inertial measurement unit data and vertical laser ranging information; projecting the three-dimensional position and posture estimated by the extended Kalman filtering to a two-dimensional plane, and using the three-dimensional position and posture as initial estimation for scanning matching; and generating indoor three-dimensional point cloud by fusing the three-dimensional position and posture and the linear scanning laser data of the inclined linear scanning laser sensor.

The application provides a kind of compound machine tool movement precision online detection method based on laser dynamic measurement, it is related to numerical control machine tool precision detection field.The method acquires the dynamic pose error of spindle end target by laser tracking measurement device fixed on the bed body in real time, with numerical control unit synchronous trigger signal as global reference, hardware level aligns laser data and servo driver feedback position;Combining pre-calibration grid static geometric error and measured dynamic incremental error, solve the real-time comprehensive space error vector of tool tip point;Based on the first-order discrete state space model, predict the future interpolation period error, after conversion by inverse gain diagonal matrix, realize the lead compensation by injecting into the position loop feedforward channel;Collect residual error after compensation, when the residual error of continuous multiple periods is out of limit, use the recursive least squares online update model parameters with time-varying forgetting factor.The application eliminates the detection and compensation lag, can adapt to working condition drift, effectively improves the dynamic machining precision of compound machine tool.