A spatial storage and retrieval method for laser point cloud data

Abstract

The present invention discloses a method for spatial storage and retrieval of laser point cloud data, which specifically includes the following steps: step 1, grid storage of laser point cloud data; step 2, establishing a quadtree structure for gridded data; step 3, spatial retrieval of point cloud data. The present invention relates to the field of laser point cloud data technology, and specifically provides a method for spatial storage and retrieval of laser point cloud data, which has the following advantages: (1) point cloud data is stored in a database to ensure data integrity and consistency; (2) gridding is combined with quadtree indexing to facilitate retrieval; (3) multiple users can access point cloud data in the same database, which is convenient for application in collaborative business.

Description

Technical Field

[0001] The invention relates to the technical field of laser point cloud data, and in particular to a spatial storage and retrieval method for laser point cloud data. Background Art

[0002] At present, laser point cloud data is mostly stored in the form of block compressed files. When applied, it is loaded and displayed according to the file name or loaded into memory for related processing. This method is limited by the size of computer memory and the data is relatively scattered.

[0003] The amount of laser point cloud data is huge, and storing it in the form of local files is not conducive to data sharing among multiple users. The flow of data is not only inefficient, but also there is a possibility of data loss and the integrity of the data cannot be guaranteed. This processing method makes it more difficult to use data in collaborative processing business. Summary of the invention

[0004] In view of the above situation, in order to make up for the above existing defects, the present invention provides a spatial storage and retrieval method for laser point cloud data, which can ensure data integrity and consistency and retrieve quickly and conveniently by combining gridded spatial storage of laser point cloud data with quadtree indexing.

[0005] The present invention provides the following technical solution: A spatial storage and retrieval method of laser point cloud data proposed in the present invention specifically comprises the following steps:

[0006] Step 1: Grid storage of laser point cloud data

[0007] (1) Obtain the range of point cloud data: that is, the minimum x, y, z coordinates and the maximum x, y, z coordinates. The minimum coordinate position is used as the base point for calculating the grid row and column layer number;

[0008] (2) Gridded point cloud data: Calculate the row, column and layer numbers of discrete points in the point cloud data, classify points with the same row, column and layer number into the same data set, and use the row, column and layer number as the identifier of this grid;

[0009] (3) Writing the divided gridded point cloud dataset into the non-relational database MongoDB;

[0010] Step 2: Create a quadtree structure for gridded data

[0011] (1) Calculate the bounding sphere of the divided grid point set;

[0012] (2) Establishing a quadtree structure based on the coordinates of the center of the grid-enclosing sphere and the grid identifier;

[0013] Step 3: Spatial retrieval of point cloud data

[0014] (1) Search for the grid identification number that meets the constraints in the quadtree structure. Since the quadtree can be accessed very quickly in memory, the grid can be located quickly.

[0015] (2) Query the corresponding data item in MongoDB according to the grid identification number to obtain the result set of the grid.

[0016] The beneficial effects achieved by the present invention using the above structure are as follows: The spatial storage and retrieval method of laser point cloud data proposed by the present invention has the following advantages:

[0017] (1) Storing point cloud data in a database ensures data integrity and consistency;

[0018] (2) Combining gridding with quadtree indexing, retrieval is fast and convenient;

[0019] (3) Multiple users can access point cloud data in the same database, facilitating its application in collaborative business. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0021] Figure 1 This is a flowchart of the overall structure of a spatial storage and retrieval method for laser point cloud data proposed in the present invention. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0023] It should be noted that the words "front", "rear", "left", "right", "up" and "down" used in the following description refer to directions in the drawings, and the words "inside" and "outside" refer to directions toward or away from the geometric center of a specific component, respectively.

[0024] like Figure 1 As shown, the technical solution adopted by the present invention is as follows: a spatial storage and retrieval method of laser point cloud data, specifically comprising the following steps:

[0025] Step 1: Grid storage of laser point cloud data

[0026] (1) Obtain the range of point cloud data: that is, the minimum x, y, z coordinates and the maximum x, y, z coordinates. The minimum coordinate position is used as the base point for calculating the grid row and column layer number;

[0027] (2) Calculate the row, column and layer numbers of the discrete points in the point cloud data, classify the points with the same row, column and layer numbers into the same data set, and use the row, column and layer numbers as the identifier of this grid;

[0028] (3) Writing the divided grid data set into the non-relational database MongoDB;

[0029] Step 2: Build a quadtree structure for gridded data

[0030] (1) Calculate the bounding sphere of the divided grid point set;

[0031] (2) Establishing a quadtree structure based on the coordinates and identification of the center of the grid-enclosing sphere;

[0032] Step 3: Spatial retrieval of point cloud data

[0033] (1) Search for the grid identification number that meets the constraints in the quadtree structure. Since the quadtree can be accessed very quickly in memory, the grid can be located quickly.

[0034] (2) Query the corresponding data item in MongoDB according to the grid identification number to obtain the point set of the grid.

[0035] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, material or apparatus including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, material or apparatus.

[0036] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for spatial storage and retrieval of laser point cloud data, characterized in that: The specific steps include: Step 1: Grid storage of laser point cloud data (1) Obtain the range of point cloud data: that is, the minimum x, y, z coordinates and the maximum x, y, z coordinates. The minimum coordinate position is used as the base point for calculating the grid row and column layer number; (2) Gridded point cloud data: Calculate the row, column and layer numbers of discrete points in the point cloud data, and classify points with the same row, column and layer numbers into the same data set, using the row, column and layer numbers as the identifier of this grid; (3) Writing the divided gridded point cloud dataset into the non-relational database MongoDB; Step 2: Create a quadtree structure for gridded data (1) Calculate the bounding sphere of the divided grid point set; (2) Establishing a quadtree structure based on the coordinates of the center of the grid-enclosing sphere and the grid identifier; Step 3: Spatial retrieval of point cloud data (1) Search for the grid identification number that meets the constraints in the quadtree structure. Since the quadtree can be accessed very quickly in memory, the grid can be located quickly. (2) Query the corresponding data item in MongoDB according to the grid identification number to obtain the result set of the grid.

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