755 results about "Point cloud processing" patented technology

This invention disclosed a full-space intelligent detection method and system for underground drainage networks, as well as storage media, including the following steps: image acquisition, intelligent image denoising, internal pipe defect segmentation, concealed defect detection around the pipe, 3D reconstruction with volume quantification, and pipeline life prediction. This invention introduced a bionic four-wheel-drive, all-terrain detection robot that can effectively navigate through mud and flowing water-challenges that hinder traditional detection devices. By leveraging deep learning algorithms as well as various techniques of computing vision, 3D reconstruction, and point cloud processing, the system thoroughly analyzed collected data to determine defect types and precise locations. Utilizing this analysis, precise location of different defect type and quantitative measurement of their dimensions can be realized. Based on the data analysis results, a deep-learning driven model was developed for predicting pipeline longevity to support maintenance staff with timely information on pipe defects and operational lifespan.

The invention relates to the technical field of computer vision and industrial automation, in particular to a shoe sole three-dimensional gluing contour extraction system and method based on vision single-line laser, and aims to solve the problems that virtual calibration target spots cannot be accurately generated based on shoe sole geometry, the positions and sizes of the target spots are difficult to determine by combining curvature extreme values and principal component analysis in the prior art, and the production cost is low. The problem that a double-branch deep learning model cannot be adopted to fuse feature prediction transformation, and the re-projection error is increased is solved; a virtual calibration target spot is automatically generated based on sole geometry through a feature fusion calibration module, a grid is generated through point cloud processing and Poisson reconstruction, the position and size of the target spot are determined by combining a curvature extreme value and principal component analysis, a corresponding relation is established by utilizing two-dimensional and three-dimensional feature matching, initial alignment is realized through ICP and re-projection error optimization, and the target spot position and size are determined. A double-branch deep learning model is adopted to be fused with feature prediction transformation, iterative optimization is carried out through space consistency errors, and re-projection errors are reduced.

The invention relates to the field of welding, and discloses a non-standard intelligent customized welding system based on vision and surface gradient, which comprises a visual perception module used for iteratively approaching a workpiece from a preset initial height through a self-adaptive high angle shooting exploration mechanism, and combining layered grid division based on a camera view and a progressive multi-angle expansion scanning mode, collecting multi-view three-dimensional point cloud data of the workpiece; and the point cloud processing module is used for performing regional progressive registration on the multi-view three-dimensional point cloud data, implementing threshold constraint based on point cloud curvature characteristics by dynamically adjusting registration step length, and eliminating accumulative errors in combination with pose map optimization. Through self-adaptive high-angle shooting and layered grid scanning, multi-view point cloud can be automatically collected without manually presetting a workpiece model, regional registration and curvature constraint are combined, the workpiece model is constructed, a weld joint structure is automatically extracted based on surface gradient features, traditional manual labeling is replaced, a welding track is generated according to weld joint topology, and the precision is dynamically corrected.

The invention provides a high-precision intelligent welding system adapting to complex working conditions, and relates to the technical field of laser welding. The high-precision intelligent welding system adapting to the complex working conditions comprises the steps of S1, based on a high-resolution visual sensor and a laser scanner, S2, based on preprocessed point cloud data, S3, based on a workpiece three-dimensional model, S4, based on an optimized welding path and a material attribute database, S5, based on a dynamic welding parameter set, S6, based on a real-time welding track, and S7, based on multi-sensor monitoring data. S8, based on a defect detection report; and S9, based on a repaired welding result and historical welding data. According to the invention, the geometric data of the workpiece are collected through the high-resolution visual sensor and the laser scanner, the filtering algorithm is applied to remove noise to generate high-quality point cloud data, and a subsequent processing foundation is laid; workpiece features are identified by using a point cloud processing algorithm and a machine learning method based on the point cloud data, and an accurate digital model is generated through a three-dimensional reconstruction algorithm.

The invention relates to the field of measurement, in particular to a subway tunnel secondary lining section limit measurement method based on SLAM mobile scanning, which comprises the following steps: step 1, tunnel point cloud acquisition and target clustering point cloud acquisition; step 2, CPIII control point coordinate extraction; step 3, point cloud coordinate conversion; 4, performing point cloud processing, and removing noisy points and non-secondary-lining target point cloud data in the tunnel secondary-lining point cloud; and step 5, performing three-dimensional limit deviation analysis, performing spatial comparative analysis on the point cloud under the construction coordinate system and the subway secondary lining tunnel design three-dimensional model by adopting a voxelization deviation mapping algorithm, extracting a continuous deviation region through voxel clustering, and generating a three-dimensional deviation thermodynamic diagram. According to the method, the two-lining tunnel point cloud obtained by SLAM mobile scanning is accurately converted into a construction coordinate system by combining CPIII high-precision control points in the tunnel, rapid, visual and three-dimensional contrastive analysis with design of a three-dimensional model can be achieved, and visual and reliable data support is provided for line and slope adjustment, track laying and defect improvement.

The invention discloses an efficient denoising power transmission line point cloud processing method and system, and relates to the technical field of three-dimensional point cloud data processing and intelligent analysis, and the method comprises the steps: carrying out the segmentation processing of original power transmission line point cloud data, dividing the point cloud into power equipment parts, carrying out the local density analysis of the segmented point cloud, and generating a density distribution diagram; based on the density distribution diagram and the local geometric features of the point clouds, multi-stage filtering is carried out, denoising model parameters are optimized, a statistical filtering method is combined to remove noise points, curvature features and normal vector consistency features of the point clouds are extracted, classification feature vectors are generated, and based on the classification feature vectors, fine classification is carried out on the point clouds. Through data preprocessing, multi-stage filtering, self-supervised learning, feature extraction and refined classification, the technical defects of insufficient multi-modal feature fusion efficiency and poor equipment feature adaptability in power transmission line point cloud denoising are effectively solved.

The invention discloses a shield muck volume estimation method and system based on image processing, and particularly relates to the technical field of tunnel engineering monitoring, and the method comprises the steps: obtaining muck RGB and depth images through an image collection device, generating high-precision three-dimensional point cloud data through the combination of laser scanning and a multispectral technology, and calculating the muck volume through a slicing method. And fusing the mass flow and water content data of the belt weigher, dynamically generating a calibration factor, and correcting a volume estimation result in real time. The system comprises an image acquisition module, a point cloud processing module, a volume calculation module and a dynamic calibration module, has the characteristics of automation, high precision, real-time feedback and the like, effectively solves the problems of large error and slow response of a traditional manual metering and single weighing mode, and is suitable for continuous and accurate monitoring of the volume of muck in the shield construction process.

The invention relates to a point cloud static denoising method based on geometric constraint and deep learning, belongs to the technical field of point cloud processing, and solves the problem of poor point cloud denoising effect caused by poor geometric fidelity and weak generalization ability for complex noise in the prior art. Comprising the following steps: after preprocessing a to-be-denoised point cloud, dividing the to-be-denoised point cloud into a plurality of point cloud blocks with overlapping regions; for each point cloud block, performing multi-round iterative optimization on the coordinates of each point cloud by using the trained denoising network, and optimizing the predicted coordinates of each point cloud output by the denoising network in each round of iteration by using normal guided filtering and incremental updating to obtain the coordinates of each point cloud for the next round of iteration; the denoising network sequentially comprises a dynamic graph convolutional network, a first multi-layer perceptron and a second multi-layer perceptron; and after the optimization results of the point cloud blocks are fused, carrying out post-processing filtering to obtain a denoised point cloud. The point cloud denoising precision and the detail retention capability are improved.

The invention discloses a laser welding seam online quality detection system and method based on multi-mode visual fusion, and particularly relates to the technical field of laser welding seam quality detection. Comprising a data acquisition and synchronous control unit, a 2D image processing and defect detection unit, a 3D point cloud processing and size measurement unit, a multi-modal information fusion and collaborative analysis unit and a comprehensive quality judgment and output unit. According to the laser welding seam online quality detection system and method based on multi-modal visual fusion, the 2D and 3D multi-modal visual fusion technology is adopted, an improved defect detection algorithm and an accurate size measurement method are combined, welding seam surface defects are accurately recognized through 2D images, key size data are obtained by means of 3D point cloud, and the detection accuracy is improved. And the limitation of'heavy defects and light sizes' or'heavy sizes and light defects' in single-modal detection is avoided, and comprehensive and accurate evaluation of the welding seam quality is realized.

The invention relates to the technical field of point cloud processing, and discloses a point cloud data processing method, system and device for industrial three-dimensional model construction. The method comprises the following steps: carrying out denoising and down-sampling processing on original point cloud data of an industrial part to obtain preprocessed point cloud data, calculating a local geometric descriptor, converting the local geometric descriptor into an industrial feature recurrent plot through a recurrent plot algorithm, inputting the recurrent plot into a convolutional neural network to extract a high-dimensional geometric feature vector, and mapping the feature vectors into B-spline curved surface control parameters through a deep belief network, and performing point cloud reconstruction quality evaluation according to the control parameters to obtain geometric accuracy error data and curved surface reconstruction integrity data. According to the method, the problems that in existing industrial three-dimensional model construction, a point cloud data processing method lacks intelligent feature recognition, the reconstruction precision is insufficient, and manufacturing constraints are not fully considered are solved, and the recognition accuracy of geometric features of complex industrial parts and the precision of three-dimensional model reconstruction are improved.

The invention discloses a laser scanning optimization system for high-precision ellipticity and diameter measurement, and relates to the technical field of cylindrical workpiece geometric dimension measurement. Comprising a laser contourgraph assembly, the laser contourgraph assembly comprises four laser contourgraphs, the four laser contourgraphs are arranged in the circumferential direction of the cross section of a to-be-measured workpiece at equal intervals, the included angle between every two adjacent laser contourgraphs is 90 degrees, and the laser contourgraphs are used for synchronously collecting contour point cloud data of the surface of the to-be-measured workpiece; and the data acquisition module is used for receiving and summarizing the contour point cloud data. The four laser contourgraphs are arranged at equal intervals in the circumferential direction of the cross section of the workpiece and collect synchronously, so that the problems that a traditional single laser module is incomplete in rotary scanning coverage and has timeliness deviation are solved; and the point cloud processing module improves the condition of insufficient point cloud processing refinement through splicing, direction correction, section extraction, adaptive region division and arc length interception processing.

The invention discloses a dynamic graph convolution-based target point cloud completion method for a battery replacement robot, and the method comprises the steps: 1, reconstructing a complete target fastener model from a multi-view image through SFM and MVS technologies, and obtaining complete point cloud data; 2, constructing an incomplete-complete point cloud pair as training data by using a geometric constraint-based adaptive cutting strategy; 3, multi-resolution point cloud processing is adopted, and feature extraction and fusion are carried out according to three-level resolution; 4, on the basis of DGCNN dynamic graph convolution, in combination with multi-stage Edge Conv dynamic edge convolution and a Transform coding module, local geometric feature capture and global relation perception are realized; 5, point cloud generation adopts a pyramid step-by-step refining method, geometric details are added to each layer based on a previous layer result, and the number of points is gradually expanded; and 6, the training process is guided through a multi-target joint loss function, and the point cloud quality is improved while the precision is ensured. The geometric structure integrity of the point cloud of the target fastener is improved, and the operation error of follow-up operation of the battery replacement robot is reduced.

The invention provides a side slope radar point cloud data slice abnormal value complementing method based on space-time fusion, which comprises the following steps: acquiring point cloud data of a plurality of time slices obtained by scanning of a side slope radar, and mapping the point cloud data into a three-dimensional point cloud; identifying a piece of abnormal regions of the three-dimensional point cloud through statistical filtering and a density-based clustering method; constructing a point cloud processing model based on PointNet + +, generating masked data and a mask index by adopting a mask strategy, and masking off pieces of point clouds; spatial features are extracted in different regions, static space weight matrixes generated by longitude and latitude and time difference global features are fused, deformation values of mask regions are predicted through a full-connection network, and model parameters are optimized; and inputting the trained model into latitude and longitude coordinates of an abnormal region of actual missing point cloud data, performing abnormal value completion, outputting a completed deformation value, and calculating a completion precision index. According to the method, the completion effect is good, all abnormal values of multiple time slices can be completed at a time, and the calculation efficiency is improved.

The invention relates to the technical field of computer vision and three-dimensional measurement, in particular to a three-dimensional point cloud semantic segmentation and measurement reference surface intelligent fitting system and a method thereof, which are mainly applied to scenes needing accurate three-dimensional space understanding, such as topographic survey, mining, urban planning, intelligent driving and industrial detection. The system comprises a data acquisition module, a preprocessing module, a feature extraction module, a reference surface acquisition module and a pixel feature conversion module. Three-dimensional point cloud data are collected through multi-sensor fusion, and the data quality and integrity are improved. A cascade filtering preprocessing process is innovatively designed, and the data quality is remarkably improved. The feature extraction module generates semantic tags, the reference surface acquisition module calculates a probability optimal fitting plane, and the pixel feature conversion module realizes multi-scale feature output. According to the method, the three-dimensional point cloud processing precision and efficiency are effectively improved, and powerful technical support is provided for the field of computer vision and three-dimensional measurement.

The invention relates to the technical field of large-scale structure measurement, and discloses a robot laser vision three-dimensional scanning measurement system for a large-scale structural member. The system comprises a robot motion platform, a laser vision sensing module, an ambient light sensing module, a point cloud processing module and a motion control module, wherein the laser vision sensing module is arranged at the tail end of the robot motion platform. The ambient light sensing module collects ambient light intensity distribution data in real time, and the motion control module combines the data with three-dimensional model data of the large-scale structural member to be measured to generate a multi-view dynamic scanning path instruction. The robot motion platform executes an instruction to drive the laser vision sensing module to move, the module projects laser stripes to the surface of a to-be-measured structural member and synchronously collects an image, the point cloud processing module performs adaptive filtering processing on the image and then generates three-dimensional point cloud data, and the requirement for efficient and accurate measurement of a large structural member in a complex industrial environment can be met.

The invention discloses an autonomous planning algorithm for an automobile gluing path, and relates to the technical field of automobile manufacturing, and the algorithm comprises the specific steps: collecting multi-source data through a line laser radar and an industrial camera, carrying out the preprocessing and synchronous fusion, carrying out the point cloud processing and curved surface modeling, generating and optimizing a gluing path, and adjusting the deviation in real time. And finally, converting into a mechanical arm instruction, controlling an executing mechanism to move, and synchronously adjusting gluing parameters and the posture of a gun head to finish high-precision gluing operation. Through dynamic formula linkage design, full-process self-adaption of track planning and execution control is achieved, manual intervention is reduced, and the gluing quality and the production efficiency are guaranteed; meanwhile, a high-precision positioning and complex curved surface modeling system is constructed by adopting a multi-sensor fusion and curvature self-adaptive NURBS weight factor formula, so that the positioning reliability and the curved surface model precision are ensured, and the adaptation requirement of a complex gluing scene of an automobile body is met.

The invention discloses a three-dimensional point cloud processing method, system, terminal and medium for anti-wrinkle interference clothes for a massage robot, and relates to the technical field of robot perception and point cloud processing, and the method comprises the steps: obtaining the three-dimensional point cloud data of the surface of the clothes based on a depth camera, and carrying out the cutting processing, and obtaining the point cloud data after cutting; preprocessing the cut point cloud data to obtain target point cloud data, and performing cascade filtering processing to obtain optimized point cloud data; and based on the optimized point cloud data, calculating a normal vector of each point, and determining to carry out massage path planning so as to control the massage robot to execute a massage action based on the planned massage path. The method can remarkably reduce the influence of clothes wrinkles on massage direction recognition of the massage robot, and can adapt to interference of clothes made of different materials. In addition, according to the cascade filtering processing method, the calculation burden is greatly reduced while the effectiveness of normal vector estimation is ensured.

The invention discloses an aircraft dragging risk early warning method based on head-mounted display equipment and a laser radar. The method comprises the following steps: establishing an aircraft dragging process risk early warning system; calibrating an RGB camera and a laser radar; training the two-dimensional target detection model to realize two-dimensional detection; determining a target point cloud and an obstacle point cloud; determining a target collision area point cloud and a safety distance; determining the curved surface type of the risk surface; correcting the risk patterns in a partitioned manner according to the curved surface type; generating a grid; and fitting the corrected risk image to the grid, and the like. According to the aircraft dragging risk early warning method based on the head-mounted display equipment and the laser radar, a system which takes the head-mounted display equipment as a carrier, integrates multiple sensors such as the laser radar, an RGB camera and a TOF camera and integrates multiple technologies such as augmented reality, deep learning, point cloud processing and distortion correction is designed; the method has stronger visibility, real-time performance and accuracy, and can be used for monitoring and early warning various risks in a complex environment.

The invention belongs to the technical field of three-dimensional point cloud processing, and particularly discloses an automatic tooth segmentation method and system for oral cavity scanning point clouds, and the method comprises the following steps: collecting and preprocessing dentition three-dimensional point clouds, extracting point cloud features containing coordinates and normal directions, and constructing a neighborhood structure; setting a first-stage multi-branch network, and performing tooth and gingiva coarse segmentation and instance initial clustering on the point cloud; setting a second-stage semantic refinement network, and carrying out local cutting on the original point cloud; setting a boundary detection and boundary offset network to enhance a tooth contact area, and refining a boundary instance by clustering to obtain a boundary enhanced instance tag; and performing label fusion, and propagating the fused label to the original high-resolution point cloud through a nearest neighbor algorithm to obtain a final semantic label and a final instance label. By adopting the technical scheme, high-precision instance-level segmentation of the complete dentition point cloud is realized through semantic segmentation, boundary detection and clustering cooperative work based on a biased field.

The application provides a battery pack blue film defect detection method and device and electronic equipment, including: for each blue film surface of the battery pack, the following processing is performed: extracting a corresponding to-be-detected region image on the depth image of the blue film surface; performing point cloud conversion on the to-be-detected region image to obtain a to-be-processed point cloud image; using a point cloud gradient filtering algorithm to calculate a defect region of the to-be-processed point cloud image to obtain at least one defect region corresponding to the blue film surface and size information corresponding to each defect region; respectively performing defect analysis on the at least one defect region based on a defect detection standard and the size information corresponding to each defect region to determine a defect detection result corresponding to the blue film surface. The application detects defects of the battery pack blue film through a 3D point cloud processing technology, improves detection accuracy, and ensures detection stability.

The invention relates to the technical field of intelligent surveying and mapping, and discloses an intelligent surveying and mapping system for urban surveying and mapping based on Internet of Things, which comprises a data acquisition module, a surveying and mapping data processing and integration module, a surveying and mapping data analysis module, an intelligent identification module and a result output module, through the arrangement of the surveying and mapping data analysis module, point cloud data and image data in surveying and mapping data are processed and integrated to improve the accuracy of the surveying and mapping data, the surveying and mapping data of different data sources and data types are analyzed in corresponding modes, and then a point cloud processing model and an image processing model are constructed to improve the accuracy of the surveying and mapping data. According to the method, the point cloud data and the image data are analyzed respectively, and then the point cloud data and the image data are combined to obtain the final earth surface object category, so that the accuracy of urban earth surface object recognition by the surveying and mapping data is improved, and meanwhile, a foundation is laid for subsequent intelligent recognition of suspicious targets.

The invention relates to the technical field of forest resource investigation and point cloud processing, and discloses a laser radar individual tree segmentation method based on point cloud dimensionality reduction and individual tree network reforming, and the method comprises the steps: taking airborne LiDAR point cloud as a special network structure, replacing a single point with a point cloud cluster, and taking a fitting cluster circle as a hub. Dimensionality reduction processing from the three-dimensional point cloud to the two-dimensional hub is carried out, and redundant information is reduced. Three hub clustering methods (Euclidean distance, four-dimensional similarity and cosine similarity) are provided to construct individual tree hub clusters, and clustering results are mapped back to the three-dimensional point cloud to realize individual tree segmentation. The calculation pressure caused by huge point clouds is effectively relieved, and meanwhile, the segmentation precision and universality are improved; through a multi-strategy hub clustering method, the adaptability and robustness in a complex forest environment are improved; by introducing automatic precision evaluation based on convex hulls, low efficiency and subjectivity of traditional manual discrimination are avoided, objectification and automation of segmentation precision are realized, and the method has high theoretical value and application prospect.

The invention belongs to the technical field of computer vision and three-dimensional point cloud processing. The invention provides an anisotropic point cloud convolution method based on local geometric self-adaption. According to the embodiment of the invention, the principal component analysis is carried out on the neighborhood point cloud to estimate the principal direction of the local geometric structure, and the affine transformation matrix is constructed; and performing dynamic deformation on the basic kernel point of the convolution operator by using the matrix, so that the shape and the direction of the convolution kernel are dynamically aligned with the local structure of the point cloud. The convolution operator is integrated into a point cloud registration network, and can be used for feature extraction of three-dimensional point clouds, especially underwater multi-beam depth finder point clouds. The convolution operator constructed by the method can effectively capture the structure priori of the strong anisotropic point cloud, and solves the problems of structure information loss and limited feature extraction capability caused by the isotropic kernel processing the data.

The invention discloses a curved surface workpiece processing three-dimensional reconstruction method and system based on point cloud data, and relates to the field of data processing, and the system comprises an acquisition module which is used for adaptively adjusting the scanning path, sampling density and scanning distance parameters of an acquisition device, and driving the acquisition device to obtain workpiece surface point cloud data; the preprocessing module is used for hierarchically dividing the acquired point cloud data according to the curvature change of the curved surface of the workpiece, removing abnormal points in each hierarchy and complementing missing point clouds; according to the method, scanning parameters are flexibly adjusted in combination with workpiece machining area parameters, comprehensive and accurate point cloud data collection is ensured, point cloud processing can be optimized according to curvature, features are accurately extracted, a model is constructed in a layered mode, the integrity of the model is guaranteed through splicing constraint, and it is ensured that the model meets machining requirements through precision verification and correction.

The invention relates to the technical field of point cloud processing, in particular to an intelligent modeling method based on point cloud data. The method comprises the following steps: scanning the interior of the transformer substation to obtain vision field overlapping graph data; performing point cloud density distribution field construction on the view field overlapping graph data to obtain point cloud density distribution field data; performing occlusion region extraction on the point cloud density distribution field data to obtain occlusion region data; performing structure connectivity calculation on the point cloud density distribution field data according to the occlusion region data to obtain occlusion region connectivity data; performing hole residual mapping on the occlusion area communication data to obtain hole residual data; performing graph neural network reasoning on the hole residual data to obtain category reasoning data; and performing physical rule fusion according to the category reasoning data to obtain point cloud complementation data, and performing point cloud model construction to obtain a point cloud model. According to the invention, the spatial coverage integrity of the point cloud data and the recognition precision of the occlusion area in the occlusion environment are improved, so that the accuracy of point cloud modeling is improved.

The invention discloses an indoor scene point cloud blocking method, and relates to the technical field of point cloud processing. Comprising the steps of obtaining sparse point cloud data; calculating a first rotation matrix and an optimal second rotation matrix based on the sparse point cloud data; inputting single-frame point cloud data frame by frame, aligning the single-frame point cloud data to a Manhattan coordinate system through the first rotation matrix and the optimal second rotation matrix, and creating a sparse voxel grid; calculating a normal vector of each voxel center point based on the sparse voxel grid to obtain a normal vector set of the voxel center points; segmenting the sparse voxel grid by adopting an adaptive segmentation algorithm to obtain an initial point cloud block and recording a boundary value of the initial point cloud block; and according to the normal vector of each voxel central point and the boundary value of the initial point cloud block, expanding the initial point cloud block by using a region growing algorithm to obtain a point cloud block with a complete boundary target. The technical problem that a computer is difficult to read massive point cloud data is solved.

The invention discloses a robot grabbing pose generation method and device, and the method comprises the steps: obtaining a natural language grabbing task instruction, and carrying out the analysis based on a large language model, and generating a structured task parameter containing target object identification information and grabbing constraint information; performing open vocabulary target detection on the image based on the identification information, and obtaining a target area mask in combination with pixel-level segmentation; generating a target point cloud based on the mask, the depth map and the camera internal reference, carrying out depth completion or denoising, outlier elimination and threshold confirmation filtering, inputting the processed target point cloud into a grabbing pose prediction network, outputting a plurality of candidate six-degree-of-freedom grabbing poses, carrying out constraint screening according to grabbing constraints, and carrying out collision detection; and selecting the candidate with the optimal confidence coefficient from the collision-free candidates as the optimal grabbing pose to be output. Task constraints penetrate through a target extraction process, a point cloud construction process and a pose screening process, and point cloud processing and collision verification are combined, so that an executable six-degree-of-freedom grabbing pose which meets the constraints is output.

The invention relates to the technical field of ultrasonic medical robots, and discloses a superficial ultrasonic scanning path planning method and device based on an SVD-improved RRT algorithm. The superficial ultrasonic scanning path planning method is applied to ultrasonic medical robot equipment, and specifically comprises the following steps: S101, receiving a data acquisition request sent by a main control end, controlling a Kinect camera to acquire a superficial depth image, processing 20 groups of calibration board images through a Matlab calibration tool box, and executing a corner detection algorithm to identify checkerboard corners, and optimizing and solving the internal reference matrix of the infrared camera. According to the method, SVD plane fitting and an improved RRT algorithm are fused, the precision and efficiency of an ultrasonic scanning path are remarkably improved, in the point cloud processing stage, statistical filtering and voxel gravity center down-sampling synergistically act, point cloud noise is reduced by 90%, meanwhile, more than 95% of three-dimensional features are reserved, a high-fidelity data basis is provided for path planning, and the method has good application prospects. SVD plane fitting forcibly passes through the center of mass, and the error is smaller than or equal to 0.5 mm

The invention relates to a calibration method of a binocular camera and a laser radar based on a hollow ellipse calibrator, and belongs to the field of robot vision. Hamming code graticule marks are respectively arranged at four corners of one side face of a square aluminum plate of the hollow elliptical calibrator in the clockwise direction, an elliptical through hole is formed in the middle of the hollow elliptical calibrator, and the center of the elliptical through hole coincides with the center of the square aluminum plate; and the long axis of the elliptical through hole is positioned in the vertical direction. A hardware system of the calibration method comprises a binocular camera, an upper computer, a 16-line laser radar and a hollow ellipse calibrator. An image processing program, a three-dimensional point cloud processing program and a mathematical calculation program are arranged in the upper computer; the calibration method comprises the following operation steps: (1) carrying out image feature recognition on the hollow ellipse calibrator; (2) carrying out point cloud feature recognition on the hollow ellipse calibrator; (3) calculating external parameters of the camera and the 16-line laser radar; and (4) determining a calibration result. According to the calibration method, the calibration accuracy and robustness are improved, and the calibration method is suitable for practical application.

The invention discloses a reverse BIM modeling method, system and equipment for an existing building space rod system composite structure, and belongs to the technical field of BIM modeling. In order to solve the problem that in the prior art, point cloud processing software lacks a special recognition and reconstruction tool for a'ball node-rod piece 'class space rod system combined structure, and efficient and accurate reverse generation of a rod piece axis is difficult to achieve, through curvature analysis, DBSCAN clustering, RANSAC spherical surface fitting and other technologies, the position of a ball node is accurately recognized from actually measured point cloud data; and finally generating a high-precision BIM model of a space rod system combined structure according with engineering practice by combining a two-stage geometric filtering strategy according to a sphere center connecting line of sphere nodes and the space constraint of the rod piece point cloud data. According to the method, the problem that actually measured point cloud data is discrete and difficult to process is effectively solved, and reliable technical support is provided for health monitoring, digital filing and accurate modeling of the existing building space rod system composite structure.