32 results about "Terrestrial laser scanning" patented technology

A method, a system, and a program for high-fidelity three-dimensional modeling of a large-scale urban environment, performing the following steps:acquiring imagery of the urban environment, containing vertical aerial stereo-pairs, oblique aerial images; street-level imagery; and terrestrial laser scans,acquiring metadata pertaining to performance, spatial location and orientation of imaging sensors providing the imagery;identifying pixels representing ground control-points and tie-points in every instance of the imagery where the ground control-points and tie-points have been captured;co-registering the instances of the imagery using the ground control-points, the tie-points and the metadata, andreferencing the co-registered imagery to a common, standard coordinate system.The referenced co-registration obtained enables:extraction of ground coordinates for each pixel located in overlapping segments of the imagery, representing a 3D-point within the urban environment; andapplying data pre-processing and 3D modeling procedures;to create the high-fidelity 3D model of a large-scale urban environment.

The invention relates to a method for extracting tree heights of high-canopy forest plots combined with UAV images and TLS point clouds, and a method for extracting tree heights of forest plots combined with UAV images and ground lidar point clouds: simultaneously obtaining high canopy density The UAV (UAV: Unmanned aerial vehicle) image point cloud of the forest plot and the ground lidar (TLS: Terrestrial Laser Scanning) point cloud data of the plot. The two kinds of data matching and fusion are realized through the scale-invariant feature transform (SIFT) feature point matching algorithm. Based on the fused point cloud data, the improved local maximum algorithm was used to extract the height of individual trees in the plot. The invention can realize the measurement of the parameters of the forest plot from two directions of understory and canopy at the same time, and provides a new approach for more comprehensive and accurate estimation of the tree height of the subtropical high canopy density forest plot.

The invention discloses a point cloud registration method from a midpoint to an optimal plane in the data collection of a ground laser scanner, including how to search for the optimal plane corresponding to each point and establish a pairing of the points to the optimal plane; adopting a constraint weighted overall Least squares calculate plane parameters; constrained weighted overall least squares are used to calculate transformation parameters. The present invention has better registration accuracy.

The invention relates to a network configuration method for single-station ground laser scanning point cloud, which includes the following steps: defining a projection sphere and projecting the single-station laser scanning point cloud onto the projection sphere; constructing an initial triangulation; in turn inserting the data points obtained after the projection of point cloud, which includes three steps; searching a triangle containing the data points to be inserted; locally reconstructing a triangulation, that is ,after finding the triangle containing the data points to be inserted ,connecting the points with the three sides of the triangle to form three new triangles; carrying out sphere LOP local optimization for the new triangulation obtained by local reconstruction; deleting six intersecting points constructing the initial triangulation to obtain a Delaunay triangulation of the projection-point sphere; projecting back the data points in the Delaunay triangulation of the projection-point sphere to the initial positions to obtain a target triangulation of the single-station laser scanning point cloud. The technology scheme provided by the invention, which can greatly improvethe efficiency of reconstructing object surface with ground laser scanning technique, is of great significance in reverse engineering technology.

The invention discloses a bilateral filtering de-noising method of point cloud data based on KDTree when a ground laser scanning technology is used to carry out measured object surface reconstruction. In a bilateral filtering algorithm, a scattered point cloud data point field is established through the KDTree so as to obtain a field point set of the data point. An inverse iteration method is used to calculate the field point so as to obtain a normal vector ni. A filtering function parameter and a filtering Gauss parameter are calculated so as to obtain a bilateral filtering power factor lambda. Finally, a new data point after the filtering can be obtained, wherein the new data point p'i=pi+ lambdani. The bilateral filtering method of the invention is used to filter the noise of the point cloud data. The method is simple and effective. An operation speed is fast. A characteristic can be maintained and simultaneously the noise can be removed. The method is suitable for processing the noise in a small scope.

The invention discloses a multi-platform point cloud data fusion method and relates to the fields of mapping and engineering surveying. The method comprises the following steps: data collecting: original data of ground objects and landform in an object region are obtained through data acquisition equipment and fixed type ground laser scanning equipment carried on mobile platforms; data preprocessing: preprocessing work such as engineered organizational management, filtering and noise reduction and the like are respectively performed on the collected original data; data fusion: after filtering and noise reduction to the point cloud data are performed, the point cloud data is accurately analyzed, point cloud data with highest accuracy is used as a basis to carry out accuracy rectification on other data, in addition, coordinate transformation of data obtained by the fixed type ground laser scanning equipment is achieved on the basis of the mobile platforms, and that point coordinate transformation without field control is achieved. The multi-platform point cloud data fusion method has the advantages that point cloud data collected by different platforms at different time are fused, so as to enable respective advantage complementation to be achieved, further comprehensive utilization of date is further improved, and fusion using of the multi-platforms and multi-scale point cloud data can be achieved.