870 results about "Lidar point cloud" patented technology

Apparatus for processing of a LiDAR point cloud of a ground scan, comprises: a point cloud input for receiving said LiDAR point cloud, a ground filter for filtering out points that belong to the ground from said point cloud, thereby to generate an elevation map showing features extending from the ground, an automatic feature search and recognition unit associated with said three dimensional graphical engine for searching said elevation map of said three-dimensional model to identify features therein and to replace points associated with said feature with a virtual object representing said feature, thereby to provide objects within said data; and a three-dimensional graphical renderer supporting three-dimensional graphics, to generate a three-dimensional rendering of said ground scan.

The invention provides a manufacturing method of a true digital ortho map (TDOM) based on a light detection and ranging (LiDAR) point cloud and an aerial image. The manufacturing method comprises the following steps of: carrying out pretreatment, organization and filtering in sequence on an airborne LiDAR point cloud so as to carry out feature extraction; carrying out matching on stereo pairs of the original aerial image so as to obtain a stereo aerial image, and extracting the characteristic of the stereo aerial image, wherein the extracted characteristic of the stereo aerial image and the characteristic of the airborne LiDAR point cloud are of the same kind; carrying out registration on density point cloud of the stereo aerial image and the airborne LiDAR point cloud after filtering based on the extractive characteristic, so as to obtain a DSM (digital surface model); and manufacturing the TDOM according to the DSM. Compared with the prior art, the manufacturing method provided by the invention can rapidly generate high-quality TDOM.

The invention discloses an automatic classification method of airborne laser radar point cloud data, belonging to the technical field of airborne laser radar. In order to improve the efficiency and the precision of the automatic classification of the point cloud data, the method comprises the steps of: firstly, splitting the point cloud data; secondly, counting the each attribute information of splitting segments; and judging the category of the splitting segments according to the spatial space relationship among the splitting segments and the attribute information of the segments. Compared with the existing automatic classification method based on the points, the classification method based on the segments has higher robustness and precision.

The embodiment of the present invention discloses a high-precision map data registration relationship determination method and device. The method comprises the steps of obtaining a first characteristic point set corresponding to a target position in the laser radar point cloud data; obtaining a second characteristic point set corresponding to the target position in the image data; pairing the first characteristic point set and the second characteristic point set, and determining a registration relationship between the laser radar point cloud data and the image data according to a pairing result. According to the embodiment of the present invention, and by adopting the above technical scheme, the registration relationship between the laser radar point cloud data and the image data can be determined without needing to set a special calibration field and measure the position of a target spot artificially, the workload needed at the registration of the laser radar point cloud data and the image data can be reduced, also the registration precision of the laser radar point cloud data and the image data can be improved effectively, and the user experience is improved.

The invention discloses a classification method based on vehicle-mounted LiDAR point cloud data. The method comprises the steps that streetscape vehicle-mounted LiDAR point cloud data are obtained and preprocessed so as to remove redundant data and noise in the point cloud data, and the point cloud normal vector, curvature and density are calculated in the preprocessing process; the preprocessed point cloud data are classified; according to the point cloud normal vector, curvature and density, power lines, building vertical face points and tree points are extracted. According to the classification method, implementation is easy, calculated quantity is small, implementation is fast and effective, the foundation is laid for streetscape facet extraction, and facet extraction is more accurate.

The embodiment of the invention discloses a semantic segmentation method based on multi-scale convolutional neural networks (CNNs). The method includes: acquiring intra-modal features in high-resolution aerial-images and LiDAR (Light Detection And Ranging) point cloud data; carrying out inter-modal feature extraction and classification on the basis of the multi-scale convolutional neural networks;and using a multi-scale segmentation method to extract ground object boundaries, eliminate salt and pepper effects, and optimize classification results. By implementing the example of the invention,a method of combining the multi-scale CNNs and multi-scale segmentation (MRS) post-processing is used for semantic segmentation of the high-resolution aerial-images and the LiDAR point cloud data.

The invention discloses an automatic segmentation method for a point cloud of a facade of a large scene city building. The method comprises steps of (1) performing fusion registration on airborne LiDAR point cloud data and on-vehicle LiDAR point cloud data, (2) extracting airborn LiDAR building point cloud data from the airborne LiDAR point cloud data which goes through registration in the step (1), (3) performing segmentation on the point cloud data of a single building based on the airborne LiDAR point cloud data extracted from the step (2), (4) performing contour tracking on the single building segmented by the step (3), (5) performing simplification and normalization processing on a contour line extracted from the step (4), (6) performing rough segmentation on the point cloud of the facade of the building based on the contour line which goes through the simplification and normalization processing in the step (5), and (7) performing fine segmentation on the building facade point cloud which is roughly segmented in the step (6). The automatic segmentation method of the invention can fast and accurately separate the building facade point cloud from the on-vehicle LiDAR point cloud.

The invention belongs to the field of intelligent unmanned intelligent ships, and relates to an unmanned ship water surface target detection, identification and positioning method based on monocular camera and lidar information fusion. The detection, identification and positioning of a water surface target by an unmanned ship are influenced by distances and the fluctuation of the target, so that alidar and a monocular camera are integrated to accurately detect, identify and position the target within a sensing range. According to the method, acquired water surface target images are adopted totrain a neural network-based target detection and recognition model; the lidar employs a conditional removal filter and Euclidean clustering to obtain the position of the water surface target in a world coordinate system; and finally, a camera image information and lidar information fusion method is designed, and therefore, the method is highly robust to uncertain factors. With the method adopted, the unmanned ship is capable of accurately detecting, identifying and positioning the water surface target; and good environment perception can be realized for the target tracking, path planning andautonomous navigation of the unmanned ship. The method has a broad application prospect.

A lidar and digital camera system collect data and generate a three-dimensional image. A lidar generates a laser beam to form a lidar shot and to receive a reflected laser beam to provide range data. A digital camera includes an array of pixels to receive optical radiation and provide electro-optical data. An optical bench passes the laser beam, reflected laser beam, and optical radiation and is positioned to align each pixel to known positions within the lidar shot. Pixels are matched to form a lidar point-cloud which is used to generate an image.

The invention discloses a negative obstacle detection method based on the local structure feature of a laser radar point cloud. In collected laser radar point cloud data, three structure features including local point cloud distance hopping, local point cloud dense distribution and local point cloud height decreasing are detected line by line; on the basis of the three structure features, candidate point pairs which may belong to a negative obstacle are obtained through extraction and screening in single line laser point cloud; all candidate point pairs obtained by the laser point cloud of each line laser are clustered on the basis of point pair length consistency and spatial position consistency to obtain a negative obstacle candidate area; and through area filtering and point pair amount filtering, obtaining a negative obstacle area. By use of the method, the negative obstacle in environment can be effectively detected, and the method has the advantages of good detection success rate, small calculation cost and high instantaneity.

The invention relates to a target detection method based on laser radar and image pre-fusion. The laser radar and the camera are combined for calibration; laser radar point cloud data of the visual angle of the camera is obtained; then, before the data is input into the neural network; firstly, spherical projection is performed on a laser radar data set, dense and two-dimensional data is obtained,then an image obtained by laser radar projection and a camera image are taken as two inputs of a network to respectively learn color and three-dimensional features, and finally the features of the two modes are fused through a 1 * 1 convolution block to realize target detection based on radar and pre-vision fusion. The method is different from traditional vision and radar respective detection, finally adopts a weighted post-fusion mode, adopts two corresponding inputs of a laser radar and an image, respectively learns features, and finally performs fusion, so that the accuracy of target recognition can be improved, and the category and three-dimensional information of a target can be obtained at the same time.

The invention provides a three-dimensional bridge reconstruction method based on vehicle-mounted LiDAR point cloud data. The three-dimensional bridge reconstruction method based on the vehicle-mounted LiDAR point cloud data can be used for visualizing an acquired three-dimensional point cloud data implementing three-dimensional model on the bottom of a bridge and comprises the following steps of (1) acquiring the vehicle-mounted LiDAR point cloud data; (2) uniformly diluting the point cloud data so as to reduce data volume; (3) calculating a normal vector, curvature and density of the point cloud data and filtering out noises; (4) registering the point cloud data and diluting the point cloud data; (5) extracting surface plates of the bridge, restraining the surface plates according to priori knowledge and establishing a TIN model; (6) performing TIN model and texture image mapping; and (7) visualizing the three-dimensional model. By the three-dimensional bridge reconstruction method based on the vehicle-mounted LiDAR point cloud data, the surface plates of the bridge can be matched, incomplete data are supplemented, thick scanning data are effectively combined to thin scanning data, and the three-dimensional model of the bridge is established quickly and precisely in real time.

The invention relates to the technical field of intelligent semantic maps, more particularly to a point cloud semantic map construction method based on deep learning and a laser radar. The method comprises: step one, constructing a deep convolutional neural network; step two, carrying out laser radar point cloud preprocessing; step three, training a deep convolutional neural network model; step four, inputting a laser radar point cloud into the deep convolutional neural network model to obtain a tag of each point in the laser radar point cloud being one including a semantic annotation; step five, carrying out real-time point cloud map construction by using the laser radar point cloud including the semantic annotation so as to obtain a point cloud semantic map; and step six, after construction of the point cloud semantic map, carrying out global optimization and correction on the semantic information in the point cloud map by using voting based on a sliding window.

The invention discloses a method for inverting remote sensing forest biomass. The method comprises the following steps: on the basis of remote sensing data pretreatment, extracting characteristic variables of a vegetation canopy from a LiDAR point cloud (comprising canopy three-dimensional space information) and multispectrum (comprising spectrum information on the upper surface of the canopy) data respectively; screening the characteristic variables of the LiDAR point cloud and the multispectrum through correlation analysis, and inverting overground and underground biomass by combining the ground actually measured biomass information through a stepwise regression model. Through the adoption of the optimized inverting model of northern subtropical forest biomass, constructed by method, the 'determination coefficient' R<2> of the model can be increase by 3-24%; the forest biomass can be estimated in high precision, and the 'relative root-mean-square error' (rRMSE) can be reduced by 2-10%. The method can be applied to the fields of forestry investigation, forest resource monitoring, forest carbon reserve evaluation, forest ecosystem research and the like, and provides quantitative data support for forest sustainable management and forest resource comprehensive utilization.

Disclosed is a power line extracting and fitting method based on in-vehicle LiDAR data. The method includes the following steps: first, dividing in-vehicle LiDAR point cloud by using volume elements and determining points in all the volume elements, second, removing the volume elements without the power line point cloud according to distribution characteristics of true power lines, third, dividing the power line point cloud acquired by filtration according to power line corridors and initially clustering the power line point cloud with the AutoClust algorithm, fourth, clustering and merging the initial point cloud belonging to the same power line with a merging cluster method based on end portion fitting line segments, and fifth, recovering fractured power lines according to characteristics of the power lines to obtain point cloud capable of being used for representing a single power line finally and carrying out three-dimensional fitting based on the point cloud. The method can achieve automatic and quick extraction of a large amount of power line point cloud in the in-vehicle LiDAR data, and also can achieve accurate recognition on the single power line and accurate fitting of three-dimensional models of the power lines.

The invention discloses a three-dimensional object recognition system and method based on laser radar and monocular vision. The method comprises the following steps: projecting point cloud onto a two-dimensional image, extracting and matching features of the projected image and a monocular image, and calculating a projective transformation relationship between the projected image and the monocularimage; carrying out object recognition through rich color information of the monocular image and extracting an object area of interest; inversely calculating corresponding point cloud blocks of the object in laser radar point cloud through a transformation relationship between laser radar and a monocular, and finally outputting space position information of the point cloud blocks. The three-dimensional object recognition system and method overcome the disadvantages of difficult recognition of a three-dimensional point cloud object, and achieve recognition and positioning of an object in a three-dimensional scene by means of the mature technique of two-dimensional image object recognition and the high-precision ranging performance of laser radar.

The invention discloses an urban building change detection method based on LiDAR point cloud spatial difference analysis. The method comprises the steps that 1, LiDAR point cloud data with different time phases are respectively preprocessed, and DSM and DTM of the different time phases are obtained through a linear interpolation algorithm; 2, two periods of the DSM obtained in the step 1 are subtracted to obtain an elevation difference model; 3, spatial difference analysis is carried out on the elevation difference model, and a candidate change area is extracted; 4, spatial features of a LiDAR point cloud are used for extracting direct features and elevation grain features; 5, building and vegetation classification is achieved; 6, superposition and overlay analysis is carried out on the candidate change area, and the building change attribute is determined. The advantages of the LiDAR data on three-dimensional representation of the building complex structure and the topological relation are fully achieved, and building three-dimensional change detection is carried out.

The invention discloses a dynamic obstacle tracking method and system based on sparse laser radar data, and belongs to the technical field of dynamic obstacle tracking, and the method comprises the following steps: S1, eliminating a static background; S2, performing dynamic point cloud clustering; S3, performing convex hull extraction; S4, performing feature extraction; S5, performing data association; S6: predicting a trajectory. According to the method, static obstacle laser radar point clouds are filtered by using a grid map, and extremely sparse point clouds are filtered by a small amountof residual static point clouds through a dbscan algorithm, so the final filtering effect is enhanced; point clouds are classified into ellipses, rectangles and straight lines in a fuzzy mode by extracting angle information of point cloud convex hulls, then correct point cloud position points are obtained by assisting the point clouds in fitting the sizes of graphs, and the accuracy of data association is guaranteed. In addition, the characteristics of the nearest neighborhood and the multi-target hypothesis algorithm are integrated, the multi-target association algorithm is improved, and dataassociation work can be efficiently completed on the premise of ensuring accuracy.

The invention discloses a LiDAR point cloud filter method based on gradient and area growth. The method mainly includes the steps of firstly, acquiring the original LiDAR point cloud data; secondly, rejecting gross errors; thirdly, gridding rules; fourthly, selecting ground seed points; fifthly, performing area growth; sixthly, using the grids where the new ground points satisfying the conditions as centers and using the point, with the lowest elevation, in the grids as the new seed points for growing according to the fourth step until no points satisfy the conditions, and stopping growing; seventhly, sequentially performing area growth on seed points and centralized seed points according to the fourth step and the fifth step until the whole process is finished. By the method, the elevation threshold size is limited effectively, the calculation quantity of the whole method is lowered effectively, and the ground points and non-ground points in LiDAR point cloud data of complex cities can be separated fast. In addition, the method is simple, and the result obtained by the method is relatively small.

The invention discloses a power line three-dimensional reconstructing method based on airborne laser radar point cloud. Data of the airborne laser radar point cloud have been correctly classified. The method includes the following steps: 1, loading the correctly-classified data of the airborne laser radar point cloud and initial line track data of an overhead transmission line; 2, extracting accurate information of the positions and the number of electric towers and the track of the power transmission line, and determining the total number of spans of the power transmission line; 3, determining the two-dimensional space range and power line laser radar points of each span of the power transmission line; 4, clustering the determined power line laser radar points of the spans; 5, carrying out power line three-dimensional reconstruction. According to the power line three-dimensional reconstructing method, the number of required parameters is small, the automation degree is high, robustness and universality are better, and the power line three-dimensional reconstructing method has the advantage of being insensitive to the factors such as the number of power lines, the types of the power lines, the power line space arrangement structure, gross error points, point-cloud irregular breaking and the line length; in addition, a reconstruction model has the high reconstruction accuracy.

The invention relates to an external parameter calibration method, device and apparatus for a laser radar and a binocular camera. The method comprises the following steps of acquiring point cloud dataof the laser radar and image data of the binocular camera; obtaining a corresponding point cloud picture according to the image data; obtaining initial external parameters of the laser radar and thebinocular camera, performing coordinate conversion on the point cloud data according to the initial external parameters to obtain point cloud data under a camera coordinate system, and obtaining the initial external parameters according to the relative attitude of the laser radar and the binocular camera; and performing registration processing according to the point cloud picture corresponding tothe image data and the point cloud data under the camera coordinate system, and obtaining a conversion matrix between the laser radar and the binocular camera according to a registration processing result. Coordinate conversion is carried out on laser radar point cloud data, registration processing is carried out on the converted point cloud data and a point cloud picture corresponding to a binocular camera, a corresponding conversion matrix is obtained, and therefore external parameter calibration of the laser radar and the binocular camera can be carried out more conveniently and accurately.

The invention discloses a multi-line laser radar and multi-path camera mixed calibration method. The method comprises the following steps of S1, collecting the original image data of a multi-path camera, the point cloud data of a multi-line laser radar and the point cloud data of a static laser radar; S2, solving an internal reference model of each camera; S3, subjecting images acquired by each camera to de-distortion treatment and obtaining corrected images; S4, registering the point cloud data of the static laser radar into the point cloud coordinate system of the multi-line laser radar; S5, acquiring the position (Xs,Ys,Zs) of each camera in the point cloud coordinate system of the multi-line laser radar in the point cloud data which are well registered in the step S4; S6, selecting the pixel coordinates (u,v) of at least four target objects in the corrected images of each camera and the corresponding three-dimensional coordinates (Xp,Yp,Zp) of the target objects in the point cloud with the multi-line laser radar as an coordinate origin; S7, according to the internal reference model of each camera, the position (Xs,Ys,Zs) of each camera, the pixel coordinates (u,v) of target objects corresponding to the camera, and the corresponding three-dimensional coordinates (Xp,Yp,Zp), establishing a collinear equation. In this way, the attitude angle elements of each camera and the cosines of the camera in nine directions can be figured out. Therefore, the calibration is completed.

The invention discloses a multiple-divided-conductor automatic extraction and fine modeling method based on LiDAR point clouds. The method comprises the steps that 1, power line and power tower point clouds are extracted from the LiDAR point clouds; 2, according to the characteristics that elevations of power line points are basically the same in a local region and elevations of power tower points vary greatly in the local region, power tower point clouds are further extracted from the power line and power tower point clouds, and the power tower point clouds are removed to obtain power line point clouds; 3, the power line point clouds are subjected to space division to obtain all-phase power line point clouds, a random consistency detection method is adopted to detect noise points in the all-phase power line point clouds, and the noise points are removed; and 4, divided sub-conductor point clouds are extracted from single-phase conductor point clouds based on dichotomy, and the divided sub-conductor point clouds are subjected to catenary fitting. Through the method, the efficiency of a three-dimensional line patrol system can be improved, more precise three-dimensional coordinates can be obtained, and corridor line patrol cost of power lines can be lowered.

The invention discloses a three-dimensional space index method aiming at massive laser radar point cloud models. Aiming at characteristics of massive data and high spatial resolution of point cloud models, the method indexes data to a single three-dimensional space point by adopting a multi-level hybrid index strategy; aiming at an integrity characteristic of point cloud models, the method continuously subdivides massive cloud points from a minimum bounding rectangle (MBB) of an overall point cloud model by adopting a cotree index, and three-dimensional points in cotree index nodes are distributed uniformly in space; and aiming at a scattered characteristic and the data post-processing requirement of large-scale point cloud models, the method indexes a single three-dimensional space point by adopting a three-dimensional K-D tree, and the quick inquiry and processing of a single point coordinate and the attribute data of the single point coordinate can be achieved. On the basis of above integrated space index construction, the method adopts a three-dimensional R tree to manage the MBBs of a plurality of point cloud models in three-dimensional scenes, and a multi-level hybrid space index mode is formed finally.

The invention discloses a 3D vehicle detection method based on multi-sensor fusion, and the method comprises the steps: Step 1, obtaining the semantic information (i.e., an RGB image) of a vehicle through a camera installed on the vehicle, carrying out the scanning of the surrounding environment of the vehicle through a laser radar located at the top of the vehicle, and obtaining the precise depthinformation (i.e., laser radar point cloud) of the environment; Step 2, preprocessing the laser radar point cloud, taking Z-axis [0, 2.5] m according to the height of the automobile, and equally dividing the point cloud into five height slices along the Z-axis direction; Step 3, generating a 3D vehicle region of interest on the laser radar point cloud; Step 4, performing feature extraction on theprocessed radar point cloud and RGB image and generating corresponding feature maps; Step 5, respectively mapping the 3D vehicle region of interest to feature maps of the point cloud and the RGB image; and Step 6, fusing the feature maps of the mapping part in the step 5, and finally realizing 3D positioning and detection of the vehicle target.

Described is a system and method for detecting elevated structures, such as bridges and overpasses, in point cloud data. A set of data from a three-dimensional point cloud of a landscape is received by the system. The set of data points comprises inlier data points and outlier data points. The inlier data points in the three-dimensional point cloud data are identified and combined into at least one segment. The segment is converted into an image comprising at least one image level. Each image level is processed with an edge detection algorithm to detect elevated edges. The elevated edges are vectorized to identify an elevated structure of interest in the landscape. The present invention is useful in applications that require three-dimensional sensing systems, such as autonomous navigation and surveillance applications.

The invention discloses a method for extracting the contour of a building by integrated images and LiDAR data. The method includes (1) pretreatment of LiDAR point cloud data; (2) establishment of a contour extraction area; (3) extraction of a building line segment; and (4) screening of the building contour. The invention can effectively extract the contour of a building with complex structure, the positioning accuracy is in proportion to in proportion to the LiDAR scheme, the rates of accuracy and integrity are high, and the robustness is favorable.

The invention provides a ground object classification method based on electric power corridor airborne LiDAR point cloud dada, and relates to the airborne LiDAR point cloud data processing field. In order to solve the problem of insufficient utilization of information included in the electric power corridor airborne LiDAR point cloud dada in the prior art, the invention provides a ground object classification method based on electric power corridor airborne LiDAR point cloud data features. The method comprises the following steps: obtaining airborne LiDAR point cloud dada; removing gross error points of the point cloud dada; carrying out point cloud dada feature extraction and processing, wherein the point cloud dada features comprise single point feature and neighbourhood feature, and carrying out normalization processing on the extracted features; and selecting samples having labels from a LiDAR point cloud dada set, separating target information of power lines, vegetation, buildings and earth surface and the like in the LiDAR point cloud dada, and classifying the data set by utilizing the samples having labels to obtain class information of power lines, vegetation, buildings and earth surface and the like.