Abstract: A method, an apparatus and an electronic device for processing point cloud data and a computer readable storage medium are disclosed. The method includes: receiving first point cloud data acquired by a laser scanner; classifying the first point cloud data to obtain second point cloud data which is classified; judging if the second point cloud data at least comprises target point cloud data, and whether a distance between other point cloud data in the second point cloud data and the target point cloud data is smaller than a first preset threshold value; if yes, determining the other point cloud data as hazardous point cloud data.

Abstract: A method, an apparatus and an electronic device for processing point cloud data and a computer readable storage medium are disclosed. The method includes: receiving first point cloud data acquired by a laser scanner; classifying the first point cloud data to obtain second point cloud data which is classified; judging if the second point cloud data at least comprises target point cloud data, and whether a distance between other point cloud data in the second point cloud data and the target point cloud data is smaller than a first preset threshold value; if yes, determining the other point cloud data as hazardous point cloud data.

Abstract: Embodiments of the present invention disclose a method and device for constructing a spatial index of massive point cloud data. The method includes: partitioning acquired original point cloud data to obtain a plurality of point cloud data blocks; constructing an octree index of a current point cloud data block for each point cloud data block; and merging the octree indexes of the plurality of point cloud data blocks to obtain a spatial index structure of the original point cloud data.

Abstract: Embodiments of the present disclosure disclose a single-tree segmentation method and apparatus. The method includes: acquiring point cloud data in a region to be segmented, classifying a highest point in the point cloud data into a target tree point cloud, setting an infinite point, and classifying the set point into a non-target tree point cloud; comparing similarity between a current point to be classified and a current target tree point cloud with similarity between the current point to be classified and a current non-target tree point cloud for each point to be classified in the point cloud data, and classifying the current point to be classified into corresponding point cloud according to a comparison result; and segmenting a final target tree point cloud into point cloud data corresponding to a target tree after all points to be classified are completely classified.

Abstract: Embodiments of the present invention disclose a method and device for filtering point cloud data. The method includes: transforming point cloud data into raster data, and acquiring a lowest point in each grid to serve as an initial point; performing a morphological opening operation on the raster data, to obtain an initial terrain surface; determining, for each initial point, a current initial point as a potential ground seed point when a difference between an elevation value of the current initial point and a value of a target grid into which the current initial point falls is smaller than a preset difference threshold value; constructing an initial triangular irregular network model according to the potential ground seed point; determining a ground point based on an triangular irregular network filtering algorithm according to the initial triangular irregular network model.

Abstract: Embodiments of the present invention disclose a method and device for constructing a spatial index of massive point cloud data. The method includes: partitioning acquired original point cloud data to obtain a plurality of point cloud data blocks; constructing an octree index of a current point cloud data block for each point cloud data block; and merging the octree indexes of the plurality of point cloud data blocks to obtain a spatial index structure of the original point cloud data.

Abstract: Embodiments of the present disclosure disclose a single-tree segmentation method and apparatus. The method includes: acquiring point cloud data in a region to be segmented, classifying a highest point in the point cloud data into a target tree point cloud, setting an infinite point, and classifying the set point into a non-target tree point cloud; comparing similarity between a current point to be classified and a current target tree point cloud with similarity between the current point to be classified and a current non-target tree point cloud for each point to be classified in the point cloud data, and classifying the current point to be classified into corresponding point cloud according to a comparison result; and segmenting a final target tree point cloud into point cloud data corresponding to a target tree after all points to be classified are completely classified.

Abstract: An integrative multi-sensors Lidar scanning system includes GPS, Lidar sensor, inertial measurement unit and control and storage system; POS system is used to acquire pose and coordinate position and realize time synchronization of the system; control and storage system is produced by integrated development based on low-power development board, which is used to store the collected data and communicate with ground station through wireless network; it could also include one or several external sensors; the parts described above are all fixed on mounting rack to form the integrative multi-sensors Lidar scanning system, and the system connects to platform through the mounting rack. This system is small in size, light in weight and low in power consumption; it is of higher stability and security; it is more convenient for the users to acquire various remote sensing data; it can be widely applied to the 3D data acquisition field.