1073 results about "Point cloud processing" patented technology

The invention provides a three-dimensional point cloud data segmentation method and system based on multi-scale point features, and relates to the technical field of point cloud processing, and the method comprises the steps: obtaining original point cloud data, constructing a three-dimensional Hash index table, carrying out the self-adaptive hierarchical division through a local maximum value of a density distribution curve, and obtaining a local maximum value of a density distribution curve; noise point elimination is carried out based on a region density count value and a geometric distribution coefficient, an enhanced feature vector containing a main feature direction, a main curvature value and the like is constructed for non-eliminated points, a local geometric descriptor is constructed by adopting four spherical search regions with increasing radiuses, a three-branch space attention network is input to extract spatial features, and the spatial features are extracted; and fused feature data are obtained through a multi-level geometric feature extraction network and a bidirectional feature propagation network. And obtaining a final segmentation result through segmentation energy function optimization and a region merging strategy.

The invention discloses an object labeling method and system based on laser point cloud, particularly relates to the technical field of laser point cloud processing, and is used for solving the problem of inaccurate labeling result caused by a complex reflection phenomenon caused by a non-Lambertian surface in the prior art. The method comprises the steps of obtaining original point cloud data collected by a laser radar and performing preprocessing; identifying a non-Lambertian reflection region based on the reflection intensity information and the geometric topology feature, and carrying out filtering correction; correcting false mirror image points through multi-layer iteration correction, and reconstructing boundaries to generate corrected point cloud data; grouping and classifying target objects in the corrected point cloud data based on a clustering algorithm combining global features and local features; finely dividing the boundary of the preliminary segmentation result by adopting an attention mechanism network, and evaluating the uncertainty of a boundary region through a local mutual information entropy; and adjusting a boundary division strategy according to an uncertainty evaluation result, and generating an optimized segmentation result.

A method for mapping a three-dimensional (3D) point cloud model based on deep learning includes: extracting features of image data by using a convolutional neural network; extracting features of point cloud data by using a PointNet point cloud processing network, and constructing a 3D model of the point cloud data by using a triangular mesh; aligning the 3D model and an image spatially and temporally; performing projection mapping on the aligned 3D model and image, and superimposing location information in the point cloud data onto texture information of the image to obtain a first fused image; fusing the features of the point cloud data and the features of the image data to obtain a second fused image with a fused feature; and superimposing the first fused image onto the second fused image, and obtaining a superimposition weight by using the convolutional neural network, to generate a mapped 3D model.

The invention discloses a point cloud semantic segmentation method and system based on local neighborhood attention, and belongs to the technical field of three-dimensional point cloud processing and deep learning, and the method comprises the following steps: S101, dividing three-dimensional point cloud data into training, verification and test sets, and carrying out the preprocessing; s102, constructing a deep learning network based on local neighborhood attention, training by using the training set, and obtaining an optimal model by using the verification set; and S103, inputting the test set into the optimal model to obtain a semantic segmentation result. According to the method, the semantic segmentation model is constructed by fusing local neighborhood attention of global position coding and local position coding, so that the problem of poor space geometric structure information extraction due to the fact that a conventional model only pays attention to the relative position of points in a neighborhood is effectively solved, and the feature extraction capability of an attention mechanism is enhanced; and the robustness, generalization and segmentation accuracy of the three-dimensional point cloud semantic segmentation model are improved.

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 discloses a park sweeper positioning and environment mapping method based on multi-sensor fusion. The method comprises the following steps: S1, preprocessing multi-modal data acquired by a plurality of sensors; s2, extracting and fusing semantic features and geometric features in the multi-modal data preprocessed by the plurality of sensors; s3, performing geometric feature extraction and recognition on the point cloud data by using a VoxelNet point cloud processing algorithm; s4, performing pose estimation and track plotting by fusing multi-sensor data; s5, identifying places where the motor sweeper repeatedly passes through through loopback detection, and correcting accumulated errors; and S6, constructing a three-dimensional environment map by fusing the laser radar, the visual features and the IMU data, performing global optimization by applying a map optimization technology, correcting errors, improving map precision, and updating the map in real time. According to the invention, the autonomous navigation capability of the sweeper is enhanced, the sweeping efficiency and safety are improved, and the sweeper is suitable for various complex and dynamic park environments.

The invention relates to the technical field of constructional engineering, in particular to a steel structure construction intelligent monitoring method based on unmanned aerial vehicle inspection and point cloud processing, which comprises the following steps: S1, collecting point cloud data of geometric shape, size and position information of each part of a steel structure; s2, registering and fusing the point cloud data from different visual angles to form a three-dimensional point cloud model; s3, geometric features of the steel structure are extracted from the three-dimensional point cloud model, and a steel structure three-dimensional model is established; s4, comparing the steel structure three-dimensional model established in the S3 with a construction drawing, and identifying geometric differences; and S5, analyzing the construction deviation report, and identifying potential quality problems in construction. According to the invention, by combining the unmanned aerial vehicle inspection, the point cloud processing and the graph neural network algorithm, the quality problem in steel structure construction can be efficiently and accurately identified, the safety risk can be evaluated, and the automation and intelligence level of construction quality monitoring can be significantly improved.

A three-dimensional point cloud segmentation method and apparatus based on a locally weighted curvature and a two-point method, which method and apparatus relate to the field of point cloud processing. The segmentation method comprises: calculating a normal vector of each sample point in a three-dimensional point cloud image (S101); calculating a locally weighted curvature of each sample point on the basis of the normal vector, and comparing the locally weighted curvature with a preset curvature threshold value, so as to obtain a comparison result (S102); when the comparison result indicates that the locally weighted curvature of the sample point is greater than or equal to the preset curvature threshold value, marking the sample point as a boundary feature point, so as to obtain a boundary feature point set (S103); fitting a point cloud planar model on the basis of the boundary feature point set, and performing iterative calculation on the point cloud planar model, so as to obtain a target inner point set (S104); and segmenting the three-dimensional point cloud image on the basis of the boundary feature point set and the target inner point set (S105).

The invention discloses a high-precision granary volume calculation method and system, and relates to the technical field of laser radar technology and point cloud processing, and the method comprises the following steps: data collection; laser radar equipment is used for carrying out omnibearing scanning on a target object, and a laser point cloud data set is obtained; preprocessing the data; performing point cloud segmentation; object recognition; point cloud precision compensation: performing precision compensation on the point cloud data, the precision compensation including error compensation of laser radar precision along with distance and temperature changes; volume calculation: performing volume calculation on the recognized regular object by adopting a volume calculation formula; for the recognized irregular object, a three-dimensional grid model of the object is created through a three-dimensional model reconstruction algorithm, the grid model is adjusted, and then the volume of the grid model is calculated; according to the method, optimization is carried out from the two aspects of radar distance and temperature compensation, an error model is established, in the model, primary functions are designed from the two aspects of temperature and distance, and the precision is improved from the two aspects at the same time.

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.

Pointcloud processing, especially for use with Building Intelligence Modelling (BIM). A method of processing a point cloud to create a representation of objects in the point cloud, the method comprises: superimposing a grid of cubes over the point cloud; and for each cube in the grid, fitting a plane to the points in the cube, using the points in the cube and its neighbouring cubes, to produce a cube plane, and using the cube planes to derive a surface mesh of polygons.

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 discloses a refined residual material monitoring method and system for a cage culture location. The method specifically comprises the following steps: firstly, installing image acquisition equipment at a feeding machine, and acquiring an original RGB image of a trough by using the image acquisition equipment; and according to the acquired original RGB image of the trough, the distribution condition of the residual materials in the trough is accurately monitored in a mode of combining deep learning and a 3D point cloud processing method, and the volume and weight data of the residual materials in different areas are accurately calculated. In addition, data analysis and corresponding early warning can be carried out according to the weight of the remaining foodstuff in the zone. According to the invention, additional equipment is not needed in hardware, and the cost is greatly reduced. According to the invention, more accurate material remaining conditions of each area can be accurately provided, the feeding efficiency is improved, and the economic cost is reduced; due to the adoption of the cloud server, the storage space is not limited by hardware, and the processing speed is extremely high.

The invention provides a structure surface multi-disease three-dimensional measurement method and device, equipment and a medium. The method comprises the steps that an image sequence carrying GPS coordinate information is collected; performing depth calculation on the image sequence by using a three-dimensional reconstruction technology, and performing three-dimensional reconstruction on a scene to generate an initial three-dimensional model; carrying out disease determination and point cloud processing to obtain a three-dimensional point cloud model of a specified color and each disease point cloud instance; using a skeleton extraction method and a boundary point extraction algorithm to extract a boundary point set and a skeleton point set of the skeleton point set corresponding to the crack point cloud, and according to each obtained skeleton point set and the corresponding boundary point set, performing quantitative calculation on the length and the width of each crack; generating a spalling surface grid model by using a Poisson surface reconstruction algorithm and a hole filling algorithm, and calculating and determining the volume of the three-dimensional spalling instance according to the spalling surface grid model; and determining the normal direction of each point of the corrosion cloud instance, generating a grid model by using a Poisson surface reconstruction algorithm, and calculating the corrosion area.

A thin film type cargo containment system datum line marking method based on an unmanned aerial vehicle rapidly and accurately completes marking construction work of a thin film type containment system, and theoretical coordinate values of 16 vertexes, theoretical side lengths of 8 longitudinal sides, theoretical coordinates of a datum axis loop line and the theoretical length of a liquid dome in the X direction are obtained according to a design drawing; and actual coordinate values of 16 vertexes, actual side lengths of 8 longitudinal sides, actual coordinates of a reference axis loop line and the actual length of the liquid dome in the X direction are obtained through point cloud processing software, and when the difference value between the actual measured value and the theoretical value of the cargo hold does not exceed the inspection length, all optimal loop lines are selected for spraying. The cargo compartment modeling method based on the unmanned aerial vehicle laser radar surveying and mapping technology is more accurate, scribing work of the thin film compartment can be completed without an installation platform or depending on GTT scribing drawings and software of the GTT scribing drawings, manual construction work and types of scribing equipment tools are greatly reduced, the scribing precision is improved, and the scribing construction period is shortened.

The invention discloses a multi-sensor fusion SLAM method suitable for a dynamic rain and fog environment. A point cloud preprocessing module, an environment judgment and decision module, a data association module, an odometer module and an optimization module are included. The point cloud preprocessing module removes multipath false points, outlier noisy points and dynamic targets and estimates the radar self-speed; the environment judgment and decision module judges environment conditions and makes corresponding decisions; the data association module associates the 4D millimeter wave radar point cloud with the image feature information; the odometer module runs a self-speed pre-integral, a 4D millimeter-wave radar odometer and a visual odometer in parallel; and the optimization module constructs loopback factors through loopback detection, performs factor graph optimization in combination with each constraint factor, and outputs an accurate pose and a 3D point cloud map. According to the invention, data of multiple sensors such as the 4D millimeter wave radar, the IMU and the camera are fused, the problem that the traditional SLAM is limited in the rain and fog environment is solved, and the method is suitable for accurate positioning and 3D mapping of an intelligent automobile or robot in the rain, fog, smoke and other environments.

The invention belongs to the technical field of live-action three-dimensional model construction, and particularly relates to a live-action three-dimensional model construction method based on ground multi-source heterogeneous point cloud fusion. The method comprises the steps of unmanned aerial vehicle image acquisition and three-dimensional modeling, ground multi-source heterogeneous point cloud data acquisition, ground multi-source heterogeneous point cloud registration, ground fusion point cloud processing, and unmanned aerial vehicle image three-dimensional model and ground fusion point cloud space registration and fusion modeling. According to the method, the unmanned aerial vehicle image and the ground multi-source heterogeneous point cloud are fused, so that the problem of detail missing in large-range scene modeling is solved. The high precision of the ground point cloud and the wide coverage of the unmanned aerial vehicle image are combined, and the integrity and fineness of the model are significantly improved. In addition, the point cloud color endowing technology enhances the sense of reality of the model, and provides efficient and reliable technical support for the fields of smart cities, cultural heritage protection and the like.

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 relates to the technical field of line laser three-dimensional scanning, in particular to a reference coordinate system calibration method based on line laser three-dimensional scanning, which comprises the following steps of: calibrating camera parameters; calibrating a light plane equation of the laser; obtaining a calibration whiteboard image; establishing a reference coordinate system; and calculating a conversion matrix from the camera coordinate system to the reference coordinate system. The reference coordinate system of the line laser three-dimensional scanner can be determined only by additionally acquiring a white board image during calibration, and whether the current object is located in the measurement range or not can be determined according to the z coordinate value of the point cloud data by converting the acquired three-dimensional point cloud data of the measured object to the reference coordinate system. And the efficiency and the accuracy of subsequent point cloud processing and three-dimensional measurement are greatly improved. Therefore, the problem that an existing calibration method is low in measurement precision is solved.

The invention discloses a cultural heritage digital reconstruction system based on deep learning, and the system comprises a data collection module, a data preprocessing module, a feature description module, a modeling module, and a model optimization module. The feature description module is used for acquiring data processed by the data preprocessing module, feature extraction is performed on point cloud data through a point cloud processing network in deep learning, the data preprocessing module is used for data preprocessing, the data preprocessing comprises correction, denoising, color correction and cutting, and the modeling module is used for modeling the point cloud data according to the extracted features. Three-dimensional reconstruction of the cultural heritage is carried out, and a model optimization module is used for smoothing a generated cultural heritage model, filling the deficiency and eliminating noise; the cultural heritage digital reconstruction method has the advantages that the digital system has rich details and sense of reality, people can conveniently visit and learn, and the efficiency and precision of cultural heritage digital reconstruction are improved.

The invention discloses a robot polishing system-oriented point cloud trajectory planning method, which comprises the following steps of: establishing a robot polishing processing hardware system, and acquiring point cloud data of a curved surface composite component under a robot base mark; accurate and simplified point cloud data are obtained according to point cloud preprocessing, a point cloud processing algorithm is used for obtaining a machining pose micro-line segment, a pose optimization function is built according to robot pose constraint conditions, and an optimal machining process pose is obtained through a genetic algorithm; a position and posture transition model and speed constraint are established for prospective position and posture initialization, position and posture synchronous judgment conditions are set for determining information of a section to be lengthened, a T-shaped speed and time synchronization method is provided for position and posture synchronous adjustment and real-time machining track planning, and efficient grinding machining of the robot is achieved. According to the method, the processing pose is acquired and optimized according to the live point cloud, and pose synchronization look-ahead planning is realized; the problem that errors exist in a digital-analog generation track is solved, posture sudden change is avoided, the machining requirement is met, and the machining efficiency is improved.

The invention discloses a hybrid teaching method and system and electronic equipment, and the method comprises the steps: collecting and preprocessing teaching environment point cloud data, generating a point cloud scene model through a decoupling sparse prior guide diffusion compression model, and improving the display quality through resistance diffusion compression. And deploying a teaching management system based on the micro-service architecture to realize multi-user real-time interaction, collecting interaction behavior data, and performing deep learning analysis to generate an evaluation report. Through the point cloud processing and image optimization method, the construction efficiency and image quality of the virtual teaching environment are greatly improved, distributed deployment and real-time interaction capability of the teaching platform are realized, and technical support is provided for hybrid teaching.

The invention relates to a three-dimensional point cloud processing method, in particular to a strip-shaped point cloud splicing method based on normal vector similarity, and mainly solves the technical problems that the precision and reliability of a traditional point cloud splicing method cannot be guaranteed, the splicing process is relatively complex, the calculation cost is relatively high and the like. The method comprises the following steps of: 1, respectively acquiring two adjacent groups of original point cloud data with overlapped parts through a rotary camera; (2) obtaining a normal vector of each point in the overlapped part data by using a principal component analysis method, and further extracting feature points to obtain a feature point set; 3, performing calculation by adopting a principal component analysis method to obtain initial transformation of the target point cloud; 4, performing point cloud registration to obtain optimal rigid body transformation of original point cloud data corresponding to the target point cloud; and (5) according to the optimal rigid body transformation, alignment of overlapped parts of two adjacent groups of original point cloud data is completed, so that point cloud splicing is realized. According to the method, the calculation cost is effectively reduced, and the splicing precision is relatively high.

The invention relates to a suspension bridge sling force rapid identification method based on unmanned aerial vehicle photogrammetry, and the method comprises the steps: collecting the point cloud data of a suspension bridge through arranging a three-dimensional laser scanner on the suspension bridge, carrying out the point cloud splicing and point cloud processing of the collected point cloud data, and obtaining the coordinates of an intersection point of a main cable and a sling, and the form of the main cable; the method comprises the following steps: acquiring an intersection point coordinate, performing temperature correction on the acquired intersection point coordinate, and then expanding the corrected intersection point coordinate according to a relation between pixel precision and a distance from an unmanned aerial vehicle to a main cable to obtain an unmanned aerial vehicle waypoint so as to complete planning of an unmanned aerial vehicle route; then, obtaining side lines of the main cable and the sling by adopting an image processing method, obtaining coordinates of an intersection point of the main cable and the sling, obtaining a sling force calculation parameter, and calculating the sling force according to a sling force calculation formula. According to the suspension bridge sling force rapid identification method based on unmanned aerial vehicle photogrammetry, daily sling force detection of a suspension bridge can be faster and more accurate.