CN115422387B - Point cloud data processing method and system based on multi-dimensional point cloud fusion data - Google Patents
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Abstract
The application relates to the technical field of point cloud data processing, in particular to a point cloud data processing method and system based on multi-dimensional point cloud fusion data. The processing method comprises the following steps: receiving multi-dimensional point cloud fusion data; judging whether the compression level is more than or equal to 1, if so, decompressing the multi-dimensional point cloud fusion data, and restoring a multi-dimensional data space; continuously analyzing a header file information field of the multi-dimensional point cloud fusion data to obtain point cloud data and basic information of a multi-dimensional data space; the length setting, coordinate system scale and unit data length of the space coordinate system of the multidimensional data space in each direction are analyzed, and a multidimensional data matrix is output. The point cloud data is simplified and normalized, the point cloud data set is converted into a binary data set, analysis and processing of the point cloud data are changed from analysis aiming at each point into batched mathematical operation on numerical values, and the problems of speed of point cloud calculation, calculation of complex target objects and the like can be effectively solved.
Description
Technical Field
The application relates to the field of point cloud data processing calculation, in particular to a point cloud data processing method and system based on multi-dimensional point cloud fusion data.
Background
The existing point cloud data storage is usually based on three sets of float data formats, and directly stores the spatial coordinates of the point cloud, and when mathematical operation is performed, the coordinate data are read in sequence for calculation, but several problems are also brought about:
(1) With the increase of the point cloud density, the number of the point clouds to be processed is greatly increased, the data processing speed is difficult to guarantee, and the traditional down-sampling method can cause the point cloud data to be distorted to a certain extent, so that the delay effect of system feedback is often caused when the high-density point cloud data is processed;
(2) Even the most basic spatial geometric characteristics, such as the length and the scale of a coordinate axis, can be obtained only after the point cloud is traversed, so that redundancy can be caused by calculation of the point cloud data in each processing process, and the delay effect of data processing is further amplified;
(3) The traditional point cloud data processing algorithm is difficult to process the geometric calculation problem of irregular objects, the most basic volume calculation of irregular target objects is taken as an example, a mature point cloud processing algorithm is not used for processing similar problems, and a specific targeted algorithm is not universal, so that the development difficulty of point cloud related application is greatly increased.
In summary, conventional point cloud processing algorithms often have no simple and effective method in the face of complex target geometry operations.
Disclosure of Invention
In order to solve the problems and effectively solve the speed problem of point cloud calculation and the complex target set operation, the application provides a point cloud data processing method and system based on multi-dimensional point cloud fusion data.
In a first aspect, the present application provides a point cloud data processing method based on multi-dimensional point cloud fusion data, which adopts the following technical scheme:
a point cloud data processing method based on multi-dimensional point cloud fusion data comprises the following steps:
receiving multi-dimensional point cloud fusion data; the multi-dimensional point cloud fusion data is obtained by processing point cloud data through a preset processing mechanism;
analyzing a header file information field of the multi-dimensional point cloud fusion data to obtain a compression level of the multi-dimensional point cloud fusion data;
judging whether the compression level is more than or equal to 1, if so, decompressing the multi-dimensional point cloud fusion data, and restoring a multi-dimensional data space;
continuously analyzing the header file information field of the multi-dimensional point cloud fusion data to obtain the point cloud data and the basic information of the multi-dimensional data space;
analyzing the space coordinate system range, the space coordinate system scale and the unit data length of the multidimensional data space;
and outputting a multidimensional data matrix according to the space coordinate system range, the space coordinate system scale and the unit data length.
By adopting the technical scheme, the point cloud data is simplified and normalized, the point cloud data set is converted into a binary data set which takes a cuboid or cubic space coordinate system as a storage space and takes binary 0 and binary 1 as identifiers, the analysis and the processing of the point cloud data are changed from the analysis aiming at each point into the batched mathematical operation of logarithm values, the problem of the speed of point cloud calculation can be effectively solved, and the problems of the calculation of complex target objects and the like can be effectively solved.
Optionally, before analyzing the header information field of the multi-dimensional point cloud fusion data and obtaining the compression level of the multi-dimensional point cloud fusion data, the method further includes:
and analyzing a header file information field of the multi-dimensional point cloud fusion data to acquire a data type.
Optionally, the method further includes:
acquiring a unit point cloud volume according to the multi-dimensional data matrix; the unit point cloud volume is a unit volume obtained by space coordinate calibration;
and obtaining the volume of the target object according to the unit point cloud volume and the number of the point clouds.
By adopting the technical scheme, the unit point cloud volume is obtained through the space coordinate scales, and compared with a traditional method that the calculation method based on the original point cloud data needs to fit a complete geometric space region firstly and then carries out cubic difference calculation aiming at the coordinate value of each point, the calculation amount is simple and the calculation speed is high.
In a second aspect, the application provides a point cloud data processing system based on multi-dimensional point cloud fusion data, which adopts the following technical scheme:
a point cloud data processing system based on multi-dimensional point cloud fusion data, comprising:
the receiving module is used for receiving multi-dimensional point cloud fusion data; the multi-dimensional point cloud fusion data is obtained by processing point cloud data through a preset processing mechanism;
the primary analysis module is used for analyzing the header file information field of the multi-dimensional point cloud fusion data to obtain the compression level of the multi-dimensional point cloud fusion data;
the judging module is used for judging whether the compression level is greater than 1, and if so, decompressing the multi-dimensional point cloud fusion data and restoring a multi-dimensional data space;
the secondary analysis module is used for continuously analyzing the header file information field of the multi-dimensional point cloud fusion data to obtain the point cloud data and the basic information of the multi-dimensional data space;
the third-time analysis module is used for analyzing the space coordinate system range, the space coordinate system scale and the unit data length of the multidimensional data space;
and the output module is used for outputting the multidimensional data matrix according to the space coordinate system range, the space coordinate system scale and the unit data length.
In a third aspect, the present application provides a computer storage medium, which adopts the following technical solutions:
a computer storage medium storing a computer program capable of being loaded by a processor and performing the method according to the first and second aspects.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the point cloud data is simplified and normalized, the point cloud data set is converted into a binary data set which takes a cuboid or cubic space coordinate system as a storage space and takes binary 0 and binary 1 as marks, the analysis and the processing of the point cloud data are changed from the analysis aiming at each point into the batched mathematical operation of a logarithm value, the problem of the speed of point cloud calculation can be effectively solved, and the problems of the calculation of a complex target object and the like can be effectively solved;
2. compared with a traditional method that a complete geometric space region needs to be fitted firstly and then cubic difference calculation is carried out on coordinate values of each point, the calculation amount is simple and the calculation speed is high.
Drawings
Fig. 1 is a flowchart of a point cloud data processing method according to an embodiment of the present disclosure.
Detailed Description
The present application is described in further detail below with reference to fig. 1.
The embodiment of the application discloses a point cloud data processing method based on multi-dimensional point cloud fusion data.
As an embodiment of the processing method, as shown in fig. 1, the method includes the following steps:
100, receiving and receiving multi-dimensional point cloud fusion data; and processing the point cloud data by a preset processing mechanism to obtain the multi-dimensional point cloud fusion data.
It should be noted that, as the distance from the target object to the center of the radar gradually increases, the density of the radar lines gradually decreases, and the obtained point cloud data is more comprehensive. If the complete point cloud of the target object is obtained, the point cloud data needs to be fused with point cloud data acquired by other radar equipment or image pixel data acquired by other camera equipment to obtain multi-dimensional point cloud fusion data, so that the target object can be detected conveniently.
The method for acquiring the multi-dimensional point cloud fusion data comprises the following steps:
101, a multidimensional data space is created.
Specifically, a spatial range is determined according to an area between the maximum coordinate and the minimum coordinate of the point cloud data, a spatial coordinate system scale is determined according to the density of the point cloud data, and a multi-dimensional data space is created based on the spatial range and the spatial coordinate system scale.
And 102, loading the point cloud data into a multidimensional data space to generate a binary data set of the point cloud data.
Specifically, point cloud data is loaded into a space coordinate system, as much point cloud data as possible falls on a space coordinate system scale, if the point cloud data exists on the space coordinate system scale, the value is assigned to 1, if the point cloud data does not exist, the value is assigned to 0, and a binary data set is generated according to the assignment of the space coordinate system scale.
103, acquiring the collected data of other equipment; wherein the acquisition data comprises point cloud data and image pixel data.
And aiming at the same target object, other radar equipment is utilized to collect point cloud data, or image pixel data is obtained by camera equipment.
And 104, loading the acquired data into the multidimensional data space to generate a binary data set of the acquired data.
It should be noted that, in the case of acquiring point cloud data by using other radar devices, the binary data set of the point cloud data is generated by using the above-mentioned step 102.
Loading the pixel data of the image into a space coordinate system under the condition that the pixel data of the image is obtained by utilizing the camera equipment, and enabling the pixel data of the point cloud image to fall on the scale of the space coordinate system as much as possible; assigning 1 if image pixel data exists on the scale of the space coordinate system, and assigning 0 if image pixel data does not exist; and generates a binary data set of image pixel data based on the manner of step 102.
105, acquiring the length of point cloud data and the length of acquired data according to the binary data set of the point cloud data and the binary data set of the acquired data; and comparing based on the point cloud data length and the acquired data length to form an equal proportion corresponding relation.
Specifically, the length of the point cloud data is the length of an effective numerical value interval, wherein the effective data value interval is an interval with the value of 1 assigned to the scale of the space coordinate system, and the acquired data length is the number of the point cloud data or the number of the image pixels. Assuming that the length of the point cloud data is 5000 and the length of the acquired data is 1000, the proportional correspondence between the point cloud data and the acquired data is 5.
And 106, fusing the point cloud data and the image pixel data or fusing the point cloud data and the point cloud data to obtain multi-dimensional point cloud fusion data based on the equal proportion corresponding relation.
And 200, analyzing a header file information field of the multi-dimensional point cloud fusion data to acquire the data type.
It should be noted that each program generally consists of a header file and a definition file, the header file is used as a carrier file containing function functions and data interface declarations and is mainly used for storing the declarations of the program, and the definition file is used for storing the implementation of the program. The header file includes a plurality of fields, each field representing an identifier having an external storage type.
The data type may be encrypted data, or compressed data or fused data.
For example, when the multidimensional point cloud fusion data is inconvenient or is not published to the user, the multidimensional point cloud fusion data is encrypted, and the header file of the encrypted multidimensional point cloud fusion data is identified. And judging whether the received multi-dimensional point cloud fusion data is encrypted or not through a header file information field of the multi-dimensional point cloud fusion data, and if so, calling the encrypted data according to an interface statement of the header file information field.
And 300, analyzing the header file information field of the multi-dimensional point cloud fusion data to obtain the compression level of the binary data set.
When the point cloud data and the binary data set of the collected data need to be stored, the binary data set is compressed in order to reduce the data storage space. Wherein the compressing step comprises:
generating an effective data value interval according to the assignment of the space coordinate system scales; the effective data value interval is an interval with the value of 1 assigned to the scale of the space coordinate system; outputting a binary data set within the valid data value interval.
And compressing the binary data set in the effective data value interval to generate a binary data set corresponding to the scale on any coordinate axis of the space coordinate system.
Specifically, the binary data set of the multidimensional data space may be compressed according to the valid data value interval. Assuming that the total height is the Z-axis of 320, the range of valid data values may be much smaller than 320, and the valid data value range is only 32, only the field in the valid data value range needs to be selected to complete data output.
The binary data set within its valid data value interval may also continue to be compressed to reduce the amount of data processing again. Assuming that on 32 scales of the spatial coordinate system, a value of 1 is assigned to a scale located at 16, 4-bit binary data 1111 can be output, and the unit data length is further reduced to 4.
Originally, each point cloud data needs to store three space coordinates of X, Y and Z, the data processing has certain difficulty, and the needed storage space is more, so that the binary data set needs to be compressed.
And identifying the compressed binary data set in a header file, and judging whether the binary data set is compressed or not according to a header file information field of the binary data set.
And 400, judging whether the compression level is more than or equal to 1, if so, decompressing the multi-dimensional point cloud fusion data, and restoring the multi-dimensional data space.
Specifically, it is determined whether the compression level is greater than or equal to 1, and if so, it represents that the binary data set is compressed, and at this time, the binary data set is decompressed to restore the multidimensional data space, i.e., the spatial coordinate system. If the compression level is less than 1, it indicates that the binary data set is not compressed, and step 500 is entered.
And 500, continuously analyzing the header file information field of the multi-dimensional point cloud fusion data to obtain the point cloud data and the basic information of the multi-dimensional data space.
And 600, analyzing the space coordinate system range, the space coordinate system scale and the unit data length of the multidimensional data space.
The unit data length is defaulted to 1, but if the unit data length is fused with other data, the limitation can be flexibly set according to needs.
And 700, outputting a multidimensional data matrix according to the space coordinate system range, the space coordinate system scale and the unit data length.
It should be noted that the multidimensional data matrix is a three-dimensional space coordinate system data set, i.e. a binary data set, composed of 0 and 1, and through the multidimensional data matrix, not only various geometric features of the target object can be calculated quickly, but also various AI identification algorithms based on such features can be derived according to the multidimensional data matrix or parallel calculation can be performed after point cloud areas are segmented quickly, so as to further improve the corresponding speed of the system and other expanding usages.
In order to solve the problem of processing the point cloud data, the first problem to be solved is the shortage of the point cloud data in the storage format. Taking a PLC point cloud processing library as an example, the default processed point cloud data format is three-dimensional coordinates of data points sequentially stored in a data stream mode, that is, three coordinates of x, y, and z are used as a basis, and each coordinate occupies the length of one data type of float. When a program performs point cloud analysis, it needs to traverse the coordinates of the data points and then perform analysis according to the relationship between the coordinates, which is a very inefficient processing method.
The point cloud data is simplified and normalized by adopting the mode, namely point cloud coordinates in a multi-dimensional data space are normalized by using a standardized space coordinate system scale, and coordinate points which are possibly lost are made up in the process. And finally, converting the point cloud data set into a binary data set which takes a cuboid or cube space coordinate system as a storage space and takes binary 0 and 1 as marks (0 represents that no point cloud exists on the scale of the space coordinate system, and 1 represents that the point cloud exists on the scale of the space coordinate system).
Therefore, the analysis and processing of the point cloud data set are changed from the analysis of each point to the batch mathematical operation of logarithm values, i.e. the point cloud problem in the data space is analyzed in a pure mathematical way, and the process can be further accelerated in a matrix operation way.
As another embodiment of the processing method, the method further includes:
acquiring a unit point cloud volume according to the multi-dimensional data matrix; wherein the unit point cloud volume is a unit volume obtained by space coordinate calibration;
and obtaining the volume of the target object according to the unit point cloud volume and the number of the point clouds.
Taking the volume calculation of an irregular target object as an example, in the traditional calculation method based on original point cloud data, a complete geometric space region needs to be fitted first, and then the cubic difference calculation is performed respectively for the coordinate value of each point, and the simplified calculation formula is as follows:
the volume formula when the method is used for calculation can be simplified as follows:
wherein,
is the coordinate value of the target point cloud n,
is the coordinate value of the point cloud n-1,
is the volume of the target object and is,
the number of the point clouds is calculated,
is the unit point cloud volume, namely the unit volume obtained by the space coordinate scale. As can be seen, the difference between the two is very significant in the amount of calculation, and as N increases, the speed advantage increases rapidly.
In addition, because all the space coordinate systems and the point cloud data are subjected to standardized processing, relatively simple mathematical methods can be used for performing relatively intuitive processing when point cloud data correlation calculation is processed, for example, the curvature of the point cloud data in a certain specified area is calculated, namely, the curvature of an arc in the area of the space coordinate systems can be simplified to be calculated.
In conclusion, through the optimization of the method, the design difficulty of the point cloud data correlation algorithm can be greatly reduced, and the execution efficiency of the correlation operation can be greatly improved.
Based on the point cloud data processing method, the application also discloses a point cloud data processing system based on multi-dimensional point cloud fusion data, which specifically comprises the following steps:
the receiving module is used for receiving multi-dimensional point cloud fusion data; the multi-dimensional point cloud fusion data is obtained by processing point cloud data through a preset processing mechanism;
the primary analysis module is used for analyzing the header file information field of the multi-dimensional point cloud fusion data to obtain the compression level of the multi-dimensional point cloud fusion data;
the judging module is used for judging whether the compression level is more than or equal to 1, and if so, decompressing the multi-dimensional point cloud fusion data and restoring a multi-dimensional data space;
the secondary analysis module is used for continuously analyzing the header file information field of the multi-dimensional point cloud fusion data to obtain the point cloud data and the basic information of the multi-dimensional data space;
the third-time analysis module is used for analyzing the space coordinate system range, the space coordinate system scale and the unit data length of the multidimensional data space;
and the output module is used for outputting the multidimensional data matrix according to the space coordinate system range, the space coordinate system scale and the unit data length.
The embodiment of the application also discloses a computer readable storage medium, which stores a computer program capable of being loaded by a processor and executing the operation control method of the central ferry vehicle, and the computer readable storage medium comprises the following components: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above embodiments are preferred embodiments of the present application, and the protection scope of the present application is not limited by the above embodiments, so: all equivalent changes made according to the structure, shape and principle of the present application shall be covered by the protection scope of the present application.
Claims (5)
1. A point cloud data processing method based on multi-dimensional point cloud fusion data is characterized by comprising the following steps:
receiving multi-dimensional point cloud fusion data; the multi-dimensional point cloud fusion data is obtained by processing point cloud data through a preset processing mechanism;
analyzing a header file information field of the multi-dimensional point cloud fusion data to obtain a compression level of the multi-dimensional point cloud fusion data;
judging whether the compression level is more than or equal to 1, if so, decompressing the multi-dimensional point cloud fusion data, and restoring a multi-dimensional data space;
continuously analyzing the header file information field of the multi-dimensional point cloud fusion data to obtain the point cloud data and the basic information of the multi-dimensional data space;
analyzing the space coordinate system range, the space coordinate system scale and the unit data length of the multidimensional data space;
outputting a multidimensional data matrix according to the space coordinate system range, the space coordinate system scale and the unit data length;
the method for acquiring the multi-dimensional point cloud fusion data comprises the following steps:
converting the point cloud data and the collected data of other equipment into a binary data set;
acquiring the length of point cloud data and the length of acquired data according to the binary data set of the point cloud data and the binary data set of the acquired data, and forming an equal-proportion corresponding relation;
and fusing the point cloud data and the acquired data based on the equal proportion corresponding relation to obtain multi-dimensional point cloud fusion data.
2. The method for processing point cloud data based on multi-dimensional point cloud fusion data according to claim 1, wherein: before analyzing the header information field of the multi-dimensional point cloud fusion data and obtaining the compression level of the multi-dimensional point cloud fusion data, the method further comprises the following steps:
and analyzing a header file information field of the multi-dimensional point cloud fusion data to acquire a data type.
3. The method for processing point cloud data based on multi-dimensional point cloud fusion data according to claim 1, further comprising:
acquiring a unit point cloud volume according to the multi-dimensional data matrix; wherein the unit point cloud volume is a unit volume obtained by space coordinate calibration;
and obtaining the volume of the target object according to the unit point cloud volume and the number of the point clouds.
4. A point cloud data processing system based on multi-dimensional point cloud fusion data is characterized by comprising:
the receiving module is used for receiving multi-dimensional point cloud fusion data; the multi-dimensional point cloud fusion data is obtained by processing point cloud data through a preset processing mechanism;
the primary analysis module is used for analyzing the header file information field of the multi-dimensional point cloud fusion data to obtain the compression level of the multi-dimensional point cloud fusion data;
the judging module is used for judging whether the compression level is more than or equal to 1, and if so, decompressing the multi-dimensional point cloud fusion data and restoring a multi-dimensional data space;
the secondary analysis module is used for continuously analyzing the header file information field of the multi-dimensional point cloud fusion data to obtain the point cloud data and the basic information of the multi-dimensional data space;
the cubic analysis module is used for analyzing the space coordinate system range, the space coordinate system scale and the unit data length of the multidimensional data space;
the output module is used for outputting a multidimensional data matrix according to the space coordinate system range, the space coordinate system scale and the unit data length;
the method for acquiring the multi-dimensional point cloud fusion data comprises the following steps:
converting the point cloud data and the collected data of other equipment into a binary data set;
acquiring the length of point cloud data and the length of acquired data according to the binary data set of the point cloud data and the binary data set of the acquired data, and forming an equal-proportion corresponding relation;
and fusing the point cloud data and the acquired data based on the equal proportion corresponding relation to obtain multi-dimensional point cloud fusion data.
5. A computer storage medium, characterized in that: a computer program that can be loaded into and executed by a processor in a method according to any one of claims 1 to 3.
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