558 results about "Global coordinate system" patented technology

The invention provides a property system virtual-real mapping inspection method, device and equipment based on digital twinning and a medium, and belongs to the technical field of property inspection. Real-time mapping of an equipment entity and a virtual model is achieved through layered design of a physical layer, a data layer, a twinning layer and an application layer; collecting static / dynamic data and reducing noise, and establishing a global coordinate system; constructing a 1: 1 parameterized model and binding equipment attributes; the state analysis model is used for evaluating the equipment health degree, and virtual-real identification and work order visualization are triggered when abnormity occurs; the inspection path is dynamically optimized, and the processing efficiency is improved in combination with AR assistance and dual-stage verification; and finally, updating the model weight and adjusting the inspection strategy through data-driven acceptance feedback. Real-time synchronization and intelligent decision making of the equipment state are realized, the inspection efficiency is improved, the fault omission ratio is reduced, the resource allocation is optimized, and the operation and maintenance transparency and reliability are enhanced.

The invention relates to the technical field of image compensation, in particular to a reflection compensation method based on image processing, and provides the following scheme: acquiring an environment panoramic image by using a panoramic camera to establish a scene global coordinate system, and acquiring a multi-view original image in combination with a camera array arranged in the circumferential direction of an object; determining a rotation symmetry axis according to the multi-view contour features, reconstructing a three-dimensional geometric body, and calculating normal distribution and curvature change; generating a prediction image based on diffuse reflection and specular reflection hypothesis in the surface expansion domain through virtual visual angle disturbance, identifying a reflection region according to color and gradient consistency, and generating a reflection mask; and performing texture reconstruction on the reflective area by using geometric registration and multi-view compensation, and finally performing splicing and fusion to obtain a non-reflective high-fidelity panoramic image. The method does not need to change the field illumination condition, and can achieve the precise recognition and compensation of the complex curved surface reflection in the cultural relic in-situ collection environment.

The invention relates to the technical field of computer vision and artificial intelligence, and discloses a computer vision-based hundred-million-pixel panoramic situation awareness method and system, and the method comprises the steps: synchronously collecting original local field-of-view images through multiple cameras; independently executing target detection and semantic segmentation in an original image domain; constructing local-to-global coordinate mapping based on the calibration parameters; projecting the target features to a global coordinate system and detecting a cross-view-field target; extracting multi-view-field texture, edge and color features, and performing weighted fusion according to a visible area; according to the method, through identification preposition and semantic level feature fusion, geometric splicing distortion is avoided, the cross-boundary identification precision is improved, the calculation load is reduced, target continuous tracking and situation real-time output in a high-density scene are realized, and the method has high engineering suitability and industrial popularization value.

The invention discloses an interferometer error calibration method and device and a computer readable storage medium, and belongs to the technical field of measurement equipment.The method comprises the steps that measurement data collected when an interferometer moves on the x / y axis of a motion table is obtained, and x / y axis compensation amount is generated through calculation processing; meanwhile, a coordinate system transformation matrix is established and optimized by using data of a marking plate arranged on a motion table, and x-axis and y-axis non-orthogonal parameters are obtained. And then the compensation amount and the non-orthogonal parameters are input into a preset kinematic model, and the accurate position of the motion platform under the global coordinate system is calculated in real time. And when a preset updating condition is met, dynamically updating the error compensation value of the interferometer through the deviation between the actual position detected by the visual system and the calculated position. Reflector surface type error compensation and non-orthogonal parameter correction are fused, and high-precision error compensation calibration of the interferometer is achieved.

The invention discloses a high-precision point cloud map construction method based on pose map optimization, and the method comprises the steps: preliminarily detecting the observation reliability of each frame of GNSS, and screening credible data; a priori factor is constructed for initial position GNSS signal quality, and map coordinate anchoring is completed; estimating a relative pose between adjacent frames, and constructing an inter-frame constraint edge; scanning context global descriptors are introduced to realize efficient screening and accurate matching of loopback candidate frames; further evaluating the quality of the GNSS by using a loopback detection result and the pose of the laser odometer, setting a GNSS factor weight, and constructing a GNSS factor edge; constructing a complete pose graph model, performing overall optimization on all pose nodes by using a nonlinear optimization method, and correcting accumulative errors; and converting the laser point cloud into a global coordinate system, and splicing to generate a global consistent high-precision point cloud map. According to the invention, the global positioning capability of the high-quality GNSS data and the local geometric precision of the laser point cloud can be effectively fused, and the high-precision point cloud map can be continuously and stably constructed.

The invention provides a method for automatically calibrating collected data of high-precision measurement and control deformation monitoring equipment, and belongs to the technical field of wind tunnel inner profile measurement. Multi-equipment clock synchronization is realized by establishing a unified time reference system, and measurement data of different frequencies are processed by adopting a CPU-GPU heterogeneous parallel processing architecture, so that the measurement accuracy is improved. The method comprises the following steps: performing three-level alignment on time, space and precision dimensions by using a multi-level clustering algorithm, eliminating clock differences among equipment by using a clock drift correction equation set, establishing a space coordinate system conversion matrix to unify a global coordinate system, and performing adaptive data fusion according to a precision weight by using a Kalman filtering fusion algorithm. And a data quality evaluation system is established to ensure the accuracy of a calibration result, a high-precision calibration data set is finally output, and the technical problem that the measurement precision is reduced due to space-time inconsistency of heterogeneous data of multiple devices is solved.

The invention provides a multi-dimensional map generation method and device based on multi-sensor tight coupling SLAM. The method comprises the following steps: acquiring image data, point cloud data and inertial data acquired by using a predetermined sensor combination; state propagation is carried out based on the inertial data, and positioning output under a global coordinate system and an estimation result used for point cloud position correction are obtained; generating a three-dimensional map and a semantic map; geometric elements are extracted based on the three-dimensional map, and a geometric annotation map is formed by global coordinates according to the geometric elements; projecting the spatial information to a plane, and superposing ground color textures from the image data to generate a two-dimensional texture grid map; and associating the positioning output, the three-dimensional map, the semantic map, the geometric annotation map and the two-dimensional texture grid map, and providing an association result for a positioning and path planning module of the mobile equipment to call. The multi-dimensional map can be synchronously output through one-time mapping, the coordinates of the four layers are uniformly updated in real time, and the manual labeling cost is remarkably reduced.

The invention provides a high-dynamic deformation measurement system and method based on laser section contour differential scanning. The measurement system is composed of a section contour measurement unit, a calibration unit, an error compensation unit and a data processing unit, wherein the section contour measurement unit comprises a multi-sight-line structured light sensor module, a speed sensing module and a distance measurement module; the calibration unit comprises a synchronous control module, an adjustment module and a common tooth-shaped calibration target; the error compensation unit comprises a vibration detection module, a posture detection module and a temperature detection module; the data processing unit is a device with programming and computing functions. The measuring method comprises the following steps: (1) calibrating a laser plane and an image plane; (2) unifying a global coordinate system; (3) obtaining point cloud data; (4) error compensation; (5) constructing a deformation time-history model; and (6) carrying out maximum deformation global optimization. According to the measurement system and method, accurate expression and maximum value global optimization of a pavement micro-deformation three-dimensional space under the load effect can be realized.

The invention provides a small target detection method and device based on a high-resolution fusion feature map. The method comprises the following steps: dividing an image to be detected into a plurality of overlapped sub-regions and recording position information of the overlapped sub-regions; performing feature extraction and multi-scale fusion on each sub-region to generate a high-resolution fusion feature map; generating geometric parameters and initial confidence of candidate frames based on each spatial position on the image, and performing preliminary screening to obtain a candidate frame set of the sub-regions; mapping candidate frames in all the candidate frame sets to a global coordinate system according to the position information, and combining repeated candidate frames pointing to the same small target to form a global candidate frame set; for each candidate frame in the global candidate frame set, cutting a local image area in the to-be-detected image to carry out fine judgment to obtain a fine trimming confidence coefficient; and fusing the initial confidence coefficient and the refined confidence coefficient to obtain a comprehensive confidence coefficient, and outputting a final small target detection result according to a comparison result of the comprehensive confidence coefficient and an adaptive threshold value. According to the invention, the tiny target in the image can be accurately captured.

The invention discloses a free curve feature design optimization method, which comprises the following steps of: constructing a free curve feature region containing a central bus, and setting the direction and the position of the central bus to obtain a free curve feature region; a two-dimensional coordinate transformation formula is adopted to transform the free curve characteristic area from a global coordinate system to a local coordinate system with a central bus as an X axis, and bending control of the structure in any direction is achieved; a height function is utilized to describe changes of a longitudinal boundary, an implicit modeling mode is adopted to construct a region boundary with a longitudinal coordinate smaller than a height function value, and adjustability of the longitudinal form of a feature region is achieved by controlling parameters of the height function. The method has the advantages that through free curve feature modeling, flexible expression of a complex structure form and precise boundary control are achieved; meanwhile, by taking control parameters as design variables and combining an iterative optimization algorithm, the structural layout is optimized, the performance is improved, the boundary is ensured to be clear, and dual optimization of the structural performance and the design freedom degree is achieved.

The invention discloses a method for optimizing micro-crack segmentation based on deep learning and super-resolution reconstruction, and the method comprises the steps: cutting an image in real time, obtaining an image block which takes a component as a target main body, and synchronously recording a homography matrix for geometric mapping; selecting an amplification strategy to improve the resolution, and recording a scale mapping relation; inputting the enhanced image block into a double-flow network; adaptive fusion and reconstruction are carried out on the two branch features, and a high-resolution texture image is output; generating a geometrically corrected ortho-image, fusing the geometrically corrected ortho-image with original illumination information, and outputting a corrected image with a known pixel size; identifying cracks, spalling and honeycomb diseases in parallel; generating a unified defect confidence map; calculating real geometric parameters of the BIM in a BIM global coordinate system through coordinate back projection; and generating quantitative defect reports and maintenance suggestions. The method has the advantage that seamless connection between the detection result and the BIM global coordinates is realized.

The invention provides a complex heterogeneous molded surface three-dimensional vision measurement method and system, and the method comprises the following steps: (1), extracting part design model features through a semantic segmentation and intelligent recognition method, and building a to-be-measured feature set; (2) a three-dimensional measurement viewpoint planning method based on reinforcement learning is adopted to obtain a viewpoint sequence and a motion path; (3) when the local measurement system reaches a preset viewpoint, a self-adaptive single-pixel imaging feature matching method is adopted for mutual reflection and semitransparent molded surfaces; for other molded surfaces, a parameter adaptive fringe projection three-dimensional measurement method is adopted to obtain a single-viewpoint three-dimensional point cloud; (4) converting the single-viewpoint three-dimensional point cloud into a global coordinate system to realize three-dimensional point cloud splicing; (5) traversing each viewpoint of the viewpoint sequence, and executing the steps (3) and (4) to obtain a multi-viewpoint spliced three-dimensional point cloud; and (6) based on the point cloud error estimation, fusing the multi-view three-dimensional point cloud. The problem of accurate acquisition of the three-dimensional point cloud of the complex heterogeneous molded surface is solved.

The invention discloses a cylindrical roller trajectory tracking method and device, and the cylindrical roller trajectory tracking method comprises the following steps: obtaining an actual measurement displacement sequence of a key point on the end surface of a roller under a global coordinate system based on a multi-reference matching digital image correlation method; drawing a key point displacement curve about key points and time according to the actually measured displacement sequence; judging the motion state of a roller according to the shape characteristics of the key point displacement curve, dividing a smooth part in the key point displacement curve into a rolling area, and dividing a part with jumping or sharp points into a sliding area; calculating the actual revolution angular velocity and the actual rotation angular velocity of the roller based on the actually measured displacement sequence corresponding to the sliding area; according to the method and the device, the image matching precision can be improved, meanwhile, the rolling and sliding components of the roller are accurately distinguished, the precision of the track of the cylindrical roller is improved, and high-precision and visual experimental data are provided for existing dynamics and tribology models.

The invention provides a new energy construction small target full-link safety management and control method based on cross-view feature collaboration and a dynamic anti-interference closed loop, and relates to the field of new energy construction safety management, and the method comprises the steps: S1, deploying a plurality of heterogeneous sensing devices, and collecting the multi-source data of a small target in a construction region; s2, unifying small target features of different visual angles to a global coordinate system of a construction area through coordinate mapping and feature decomposition fusion according to the multi-source data collected in the step S1, and outputting an aligned small target feature map; and S3, adaptively performing multi-scale feature enhancement and lightweight processing according to the aligned small target feature map output in the step S2 and the scale and texture characteristics of the small target, and outputting an initial recognition result of the small target. Small construction targets such as bolts and buckles can be accurately identified, the cross-view space-time dislocation problem of multi-source equipment is solved, dynamic illumination and equipment vibration interference is inhibited, and full-link management and control from data acquisition, feature enhancement and precision calibration to rectification closed loop is realized.

The invention provides a strong nonlinear simulation method and device suitable for a large floating fan, and the method comprises the steps: dividing a target floating fan into a plurality of body structures according to the model parameters of the target floating fan, so as to construct a multi-body model containing a plurality of degrees of freedom, and building a whole machine load and a motion transmission path; the aerodynamic load input of each body structure is calculated, and the nonlinear dynamic response of the blade is calculated according to the aerodynamic load input of each body structure; according to the coordinate transformation relation, the nonlinear dynamic response of the blade is converted into a whole coordinate system and input into a multi-body dynamic model of the whole machine, generalized active force and generalized inertia force of each body structure in the target floating type draught fan are obtained through calculation, and the motion response of each body structure of the target floating type draught fan is obtained through simultaneous solution. According to the scheme, the motion response of each body structure of the target floating fan can be accurately determined, so that the running state of the floating fan in the deep and far sea complex environment can be accurately evaluated.

The invention discloses a tunnel point cloud high-precision splicing method based on rotation and translation constraint, and belongs to the technical field of engineering measurement and three-dimensional laser scanning. The method comprises the following steps: a data acquisition stage: acquiring absolute coordinates and relative point cloud data of each station through collaborative operation of a total station and a frame station scanner, and completing scanning of a target tunnel section; a point cloud data preliminary transformation stage: performing translation transformation on relative point cloud data of each station by using absolute coordinates of a rack station scanner, and preliminarily transforming the relative point cloud data into a global coordinate system; a two-stage registration stage based on rotation translation constraint: carrying out two-stage registration on the relative point cloud data of the adjacent sites after preliminary transformation; and a global point cloud model generation stage: unifying the relative point cloud data of all sites to a tunnel construction global coordinate system through two-stage registration, and generating a complete tunnel three-dimensional point cloud model. According to the invention, through rotation translation constraint and a two-stage registration strategy, the millimeter-level splicing precision is ensured, and the cost and the operation difficulty are reduced at the same time.

The invention discloses an air-ground collaborative observation unmanned system key target intelligent search method, which comprises the following steps that: an unmanned aerial vehicle carries a positioning module to provide a positioning coordinate, and executes a preset fixed-point cruise task; the unmanned aerial vehicle performs data acquisition on the environment of the cruising area; a height self-adaptive target detection module is adopted to carry out target detection on image data collected by the unmanned aerial vehicle, a rescue target and temporary obstacles generated after a disaster are recognized, coordinate transformation is carried out on the obstacles and target points, and a detection result is mapped to a position under a global coordinate system; transmitting the recognized target point and obstacle coordinates to a ground mechanical dog in real time through a communication module; the ground mechanical dog updates a dynamic cost map according to the received environment information to realize real-time perception of the environment; and the ground mechanical dog performs path planning based on the environment map. According to the method, efficient perception and rapid rescue of the target point can be realized, and the task execution efficiency and the system robustness in the post-disaster complex environment are remarkably improved.

The invention discloses a three-dimensional modeling method for extracting a road design center line based on Dynamo, and the method comprises the steps: extracting horizontal curve and vertical curve data from a design drawing, and writing the data into a single Excel worksheet according to a sequence from small stake marks to large stake marks; according to the pile-by-pile coordinates, the coordinates of the road center lines of all the pile numbers are exported into an Excel worksheet according to the sequence from the small pile numbers to the large pile numbers; performing forward calculation and reverse calculation to check the accuracy of the corrected coordinates in a bidirectional manner; exporting a stake-by-stake coordinate table, and storing the stake-by-stake coordinate table as an Excel file according to stake numbers, X coordinates, Y coordinates and elevations; the Dynamo program reads the coordinate data in the Excel file, and the Dynamo program reads the coordinate data in the Excel file; converting the construction coordinate system into a Revit global coordinate system; the Dynamo program reads the x, y and z coordinates of the points to generate a three-dimensional curve of the road center line, and reads the x and y coordinates of the points to generate a horizontal curve of the road center line; the Dynamo program generates road center lines of segments according to mileage stake numbers; according to the method, various defects and problems existing in existing complex geometric line type road modeling are solved.

The invention relates to the technical field related to automatic driving, in particular to a vehicle global positioning deviation correction method based on a roadside sensing result, and the method comprises the steps: obtaining the global pose of a vehicle through a 3D target detection network in which roadside equipment is fused with time sequence features, and carrying out the uncertainty quantification; meanwhile, a vehicle end adopts a tight coupling laser radar and IMU data to realize high-frequency local positioning, a time domain self-adaptive interpolation mechanism is designed for compensating time deviation aiming at the problem of roadside sensing time delay, roadside observation is constructed into a roadside sensing factor of self-adaptive covariance, and finally, through factor graph fusion optimization, a road-side sensing model is obtained. According to the present invention, by combining the IMU pre-integration factor, the laser radar odometer factor and the roadside perception factor, the accurate vehicle pose estimation of the accumulative error correction and the global coordinate system alignment is achieved, and the experiment shows that the method significantly improves the positioning precision and the robustness in the complex environment, and effectively solves the positioning drift problem when the GNSS signal is missing.

The invention provides an unmanned aerial vehicle laboratory testing method and device, and belongs to the technical field of unmanned aerial vehicle testing. The scheme comprises the following steps: determining a global reference point based on the position of a laboratory, and further determining a conversion relation between a local northeast high coordinate system and a global coordinate system and a plurality of fixed reference points; according to the fixed reference point, sequentially executing stand setting and starting of an automatic measurement tracker in a laboratory, and further obtaining real-time coordinates of the to-be-tested unmanned aerial vehicle by using the automatic measurement tracker; the GNSS signal generator generates a simulation navigation signal according to the real-time coordinates of the to-be-tested unmanned aerial vehicle and records a simulation track; the to-be-tested unmanned aerial vehicle performs indoor flight test based on the simulation navigation signal; and comparing the simulation track with a flight track recorded by a flight control system of the to-be-tested unmanned aerial vehicle so as to output a laboratory test result. The scheme does not depend on hardware modification of the unmanned aerial vehicle, the implementation difficulty is reduced, the implementation cost is saved, and controllable testing of medium and large unmanned aerial vehicles in the indoor environment is facilitated.

The invention discloses an unmanned aerial vehicle complex path planning method based on an improved RRT * fusion APF algorithm, and the method comprises the steps: firstly, obtaining an operation environment of an unmanned aerial vehicle through a binocular camera, converting the environment into a three-dimensional geometric map, building a global coordinate system for the map, and determining the coordinates of each obstacle; secondly, performing path planning by using an improved RRT * fusion APF algorithm to generate a flight path; thirdly, optimizing the track through a third-order B-spline curve smoothing strategy; and finally, inputting the optimized flight path into the unmanned aerial vehicle, and enabling the unmanned aerial vehicle to run according to the planned path. The improved RRT * fusion APF algorithm adopted by the invention optimizes the path nodes of the traditional RRT * algorithm, shortens the path planning time and length, can avoid obstacles in real time, and ensures the adaptive capacity of the unmanned aerial vehicle in a complex environment.

The invention provides an elevator guide rail digital installation method based on a laser tracker and a motion capture system, which comprises the following steps that: A1, a shaft global coordinate system is constructed, a shaft local measurement network is formed, and a lifting operation platform is provided with a guide structure matched with an installed guide rail; a2, after multiple sets of target ball space coordinate data are collected, the conversion relation between a motion capture camera array coordinate system and a laser tracker coordinate system is calculated through an optimization method based on rigid body distance constraint; a3, through a coordinate transformation and projection correction algorithm, the installation position data of the elevator guide rail support is converted into a two-dimensional projection image, and the two-dimensional projection image is projected to the well wall face through projection equipment; a4, acquiring three-dimensional coordinate data of the installed guide rail reflection marker through the motion capture camera array, and calculating a deviation matrix of an actual installation position and the design model so as to correct the installation deviation of the guide rail; and A5, the steps A2 to A4 are executed again to install the next section of guide rail, and the benchmark accumulative error is eliminated through recalibration.

A method for previewing the terrain in front of an emergency rescue vehicle in a geometric feature degradation scene relates to the technical field of road surface recognition, realizes accurate alignment of LiDAR point cloud and IMU data through timestamp synchronization and linear interpolation, and combines a spherical projection model and a degradation perception complementary feature selection algorithm to realize the previewing of the terrain in front of the emergency rescue vehicle. Converting the three-dimensional point cloud into a robust intensity image and extracting gradient significant features; based on a double-observation secondary filter frame, a motion state is predicted by utilizing IMU forward propagation, a point cloud geometric residual error and an intensity image luminosity residual error are synchronously fused, timestamp deviation is compensated through back propagation, and a multi-source observation model under a global coordinate system is constructed. Finally, the error state is iteratively optimized to realize collaborative output of the high-precision odometer and the three-dimensional terrain map, and the problems of data asynchronism, feature degradation and dynamic interference in a complex scene are effectively solved.

The invention discloses a cross-device multi-target tracking method and system. The method comprises the following steps: converting vehicle driving data collected by a camera and a radar into trajectory data under a global coordinate system; dividing all the sensing devices into a plurality of adjacent device pairs; checking the time dimension and the space dimension of the upstream track and the downstream track which are respectively formed by the adjacent equipment pair in sequence; and if the upstream track and the downstream track meet verification, splicing the track sequence according to a timestamp sequence to perform fusion of the global track, otherwise, temporarily storing the track as an isolated track independent of the global track. The system is used for implementing the method. The method has the advantages that feature extraction is not needed, the calculation complexity is low, and the real-time processing requirement of a large-scale sensing network can be met. Spatial similarity calculation methods are respectively designed for devices of the same type and different types, the cooperation requirements of mainstream sensing devices in an intelligent traffic scene are covered, and the compatibility is high.

The invention relates to a scene deformation risk early warning method and device based on a mobile robot, and the method comprises the steps: obtaining perception information uploaded by at least one mobile robot, the perception information comprises real-time three-dimensional point cloud data collected by a 3D perception sensor carried by the mobile robot and a high-precision pose of the mobile robot calculated in real time based on the same frame of point cloud data; according to the pose of each mobile robot, converting the real-time three-dimensional point cloud data of at least one mobile robot into a unified global coordinate system, and performing splicing and fusion through a point cloud registration and fusion algorithm so as to incrementally construct and dynamically update a current three-dimensional morphology model; comparing the current three-dimensional shape model with a historical reference three-dimensional shape model, and calculating the deformation quantity of the target structure body in the operation scene; and when the deformation quantity exceeds a preset safety threshold value, risk early warning information is generated and output, so that the monitoring efficiency can be improved, and real-time early warning can be realized.

The invention provides a video point cloud data fusion and curved surface reconstruction method based on a digital twin scene, and belongs to the technical field of video processing. Then, inertial data is mapped to a Lie group manifold space, and a continuous trajectory model is constructed by using B spline fitting; based on the model, accurately calculating a first pose transformation matrix at the exposure moment of each row of pixels and a second pose transformation matrix of each laser point; then, projecting the point cloud to a global coordinate system by using the second matrix, and generating multi-dimensional feature mapping data of space-time registration by combining the first matrix, the internal reference and a perspective principle; and finally, inputting the data into a voxelization truncated symbol distance field, synchronously updating a distance value and a color weight, and generating a high-fidelity digital twin three-dimensional curved surface by extracting a zero level set contour surface.

The invention provides a positioning method and device and a storage medium, and the method comprises the steps: a first cloud server obtains environment information uploaded by a terminal, carries out the environment reconstruction through the environment information, and obtains a map under a global coordinate system, the global coordinate system is a position coordinate system of map data after environment reconstruction; in response to starting of the terminal, the first cloud server obtains a first coordinate of the terminal in a local coordinate system every preset time interval, and determines a target coordinate difference value corresponding to the first coordinate in a global coordinate system; and the terminal receives the target coordinate difference value sent by the first cloud server, and corrects the first coordinate based on the target coordinate difference value to obtain a second coordinate positioned by the terminal. According to the method and the device, deviation accumulation or positioning loss of the positioning position is avoided, the positioning precision is improved, the situations of mode crossing and drifting in the virtual picture are further avoided, a user does not need to position through a computer backpack, and the user experience is improved.

According to the method and the system for acquiring the overall spatial pose of the bridge girder erection machine, a global coordinate system is established by taking a connecting line of theoretical center points of any two adjacent bridge piers as an original point, and light reflecting materials are mounted on the bridge piers with different structures to enhance light reflecting characteristics; two-dimensional point cloud data of piers of different structures in a construction area are collected through a current laser radar sensor, and actual center points of the piers of different structures are correspondingly obtained after screening and clustering processing; and matching the actual center point with the theoretical center point to obtain the pose of the current laser radar in the global coordinate system, determining the dynamic poses of the front, middle and rear support legs according to a preset front support leg reference point and a radar support leg set installation relation, and obtaining the overall space pose of the bridge erecting machine according to the three-point configuration relation and the bridge erecting machine path displacement. Compared with the prior art, accurate positioning can be realized through mutual cooperation of the two-dimensional laser radar and the reflective structure, and the positioning cost is reduced while the universality, the positioning precision and the mapping efficiency are improved.

Disclosed is interactive labeling of a 4D dynamic object based on time series data, which aims at time series-related point cloud dynamic object data. Multi-frame local point clouds in the same time series are transformed into the same global coordinate system with corresponding poses to obtain global point clouds in the same time series are obtained, which clearly shows the moving trajectory of the dynamic object. Taggers can label key 3D boxes based on the moving trajectory of the dynamic object, and automatically generate 3D prediction boxes of other frames based on these key 3D boxes, which significantly reduces the number of frames that need manual operation and solves the problem that 3D prediction boxes generated based on deep learning model are inaccurate and efficiency can hardly be improved.

The invention relates to the technical field of automatic driving, and discloses a vehicle driving scene generation method based on an automatic driving simulation environment, and a track generation method, system and equipment, and the vehicle driving scene generation method comprises the steps: determining a dense depth map according to a real vehicle driving scene image and a sparse radar point cloud corresponding to the real vehicle driving scene image; generating a dense three-dimensional point cloud under the global coordinate system according to the dense depth map; and generating a future observation image of the vehicle driving scene based on the dense three-dimensional point cloud. According to the scheme, the vehicle driving simulation scene which is stable in geometric structure and good in picture consistency can be generated in the vehicle automatic driving closed-loop simulation process, and the consistency and controllability of driving scene generation are improved.