CN111882623A - Compression method of three-dimensional space measurement result data - Google Patents
Compression method of three-dimensional space measurement result data Download PDFInfo
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Abstract
The invention discloses a compression method of three-dimensional space measurement result data, which comprises the following steps: acquiring point cloud data, namely scanning an object by using laser radar equipment to obtain initial data, and converting the initial data by using a trigonometric function to obtain rectangular coordinate system data; carrying out uniform grid division on the point cloud data, carrying out proportional division on the point cloud data according to the transmitted parameters, and adopting a uniform division mode for the division of the point cloud data; point cloud data discrete abnormal points are removed, discrete points deviating from the whole body and abnormal points exceeding normal values are generated in the running scanning process of the laser radar, and discrete abnormal points are removed by using difference values; extracting characteristic points, namely judging according to point location data values in the grids, and if the maximum and minimum difference values in the grids are larger than the height value, indicating that the characteristic points exist in the grids; and point cloud compression, namely compressing the points in a certain proportion according to the divided grids, and adding the extracted feature points into the compressed point cloud data.
Description
Technical Field
The invention relates to the technical field of three-dimensional space data processing, in particular to a compression method of three-dimensional space measurement result data.
Background
In recent years, with the rapid development of three-dimensional scanning equipment, the scanning cost of three-dimensional data is greatly reduced, the acquisition of the three-dimensional data becomes easier and faster, point clouds are gradually widely accepted and used as an effective and high-quality three-dimensional data acquisition and storage mode, the processing technology of the point cloud data is mature, a large amount of point cloud data needs to be compressed and simplified in the application process, most three-dimensional point cloud data compression in the prior art adopts a compression mode of mean value compression or random sampling, the mean value compression algorithm can well improve the compression ratio of the data, but the data with some characteristic points can be compressed, so that the error value is increased to cause the later-stage calculation deviation, the random sampling compression is a simple compression mode, but when unordered compression is used, the compression ratio and the error value of the data cannot be well guaranteed, the above methods all have the problems of low and uncontrollable point cloud data compression efficiency, large error value and disordered point location sequence.
Disclosure of Invention
Aiming at the technical problems in the related art, the invention provides a method for compressing three-dimensional space measurement result data, which can control the compression ratio of point cloud data, reduce the error value of the point cloud data after being compressed and enable the point cloud data to be serialized after being compressed.
In order to achieve the technical purpose, the technical scheme of the invention is realized as follows: a compression method for three-dimensional space measurement result data is characterized by comprising the following steps:
s1: acquiring point cloud data, namely scanning an object by using laser radar equipment to obtain initial data, and converting the initial data by using a trigonometric function to obtain rectangular coordinate system data;
s2: carrying out uniform grid division on the point cloud data, carrying out proportional division on the point cloud data according to the transmitted parameters, and adopting a uniform division mode for the division of the point cloud data;
s3: point cloud data discrete abnormal points are removed, discrete points deviating from the whole body and abnormal points exceeding normal values are generated in the running scanning process of the laser radar, and discrete abnormal points are removed by using difference values;
s4: extracting characteristic points, namely judging according to point location data values in the grids, and if the maximum and minimum difference values in the grids are larger than the height value, indicating that the characteristic points exist in the grids;
s5: and point cloud compression, namely compressing the points in a certain proportion according to the divided grids, and adding the extracted feature points into the compressed point cloud data.
Further, S1.1 obtains the three-dimensional laser radar point cloud data, and performs function conversion on the angle value and the transmitting distance value data transmitted by the laser radar.
Further, S2.1 is divided evenly according to the transmitted parameters, if the parameter value is smaller than the maximum rectangular coordinate value and is not equal to zero, the compression algorithm is executed, and if the parameter value is equal to the rectangular coordinate value, the compression algorithm is not executed.
Furthermore, in the S3.1 laser radar operation scanning process, due to the influences of light, water vapor interference and environmental factors, discrete points deviated from the whole body and abnormal points exceeding normal values are eliminated through difference values.
Further, in the S4.1 data compression process, if the loss of the feature point increases the error value of the compression algorithm, the determination is performed according to the point data value in the grid, and if the maximum and minimum difference values in the grid are greater than the height value, it is determined that the feature point exists in the grid, and feature extraction is performed.
Further, in S5.1, when point cloud data is compressed, compression is carried out according to a certain proportion of divided grids, if 100 point locations exist in the grids, the compression rate is 10, the number of the remaining point locations after compression is 10, and the point cloud data is uniformly compressed;
s5.2, after the point cloud data is compressed, adding the extracted feature points into the compressed point cloud data, and storing the point cloud data to generate serialized point cloud data.
The invention has the beneficial effects that: in view of the defects in the prior art, the method has the following advantages:
1) the compression efficiency of the point cloud data is controllable, and the compression rate is used as an optional parameter to be transmitted into the algorithm, so that the compression efficiency is controlled, and the higher the compression efficiency is, the better the compression efficiency is, but for the compression of the point cloud data, the higher the compression efficiency is, the more the lost information point positions are, and therefore a balanced compression point can be found for each point cloud by using the controllable compression efficiency;
2) point location serialization is carried out after point cloud compression, and the compressed point cloud data should be an ordered point cloud, so that the point cloud is converted into a three-dimensional graph for output in the later period;
3) the error value of the point cloud data is reduced, the method can keep the characteristic points in the area while controlling the compression rate, and eliminate the discrete points and the abnormal points, thereby ensuring the compression efficiency, ensuring the overall characteristic of the point cloud not to change greatly and ensuring the error value to be reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic diagram of a point cloud data original point of a method for compressing three-dimensional space measurement result data according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a point cloud data grid of a compression method for three-dimensional space measurement result data according to an embodiment of the invention;
FIG. 3 is a schematic diagram illustrating a point cloud data discrete anomaly of a compression method for three-dimensional space measurement result data according to an embodiment of the present invention;
FIG. 4 is an exemplary diagram of a point cloud data feature point of a compression method for three-dimensional space measurement result data according to an embodiment of the invention;
FIG. 5 is a schematic diagram illustrating a point cloud compression method for compressing three-dimensional measurement result data according to an embodiment of the present invention;
fig. 6 is a schematic view of compressed point cloud data of the method for compressing three-dimensional space measurement result data according to the embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
As shown in fig. 1 to 6, the compression method of three-dimensional space measurement result data according to an embodiment of the present invention includes the following steps:
step one, point cloud data is obtained;
step two, uniformly meshing point cloud data;
step three, removing discrete abnormal points of the point cloud data;
step four, extracting the characteristic points;
and step five, point cloud compression.
In a particular embodiment of the present invention,
acquiring point cloud data, namely scanning an object by using laser radar equipment to obtain initial data, and converting an angle value transmitted by the laser radar and data of a transmitting distance value by using a trigonometric function to obtain rectangular coordinate system (x, y and z) data, as shown in figure 1;
in a particular embodiment of the present invention,
the method comprises the following steps of uniformly meshing point cloud data, wherein the point cloud data are divided in proportion according to input parameters based on algorithm requirements, the point cloud data are divided uniformly, input parameters n are uniformly divided, if n is smaller than the maximum rectangular coordinate values x and y and is not equal to zero, a compression algorithm is executed, and if the parameter values are equal to the rectangular coordinate values, the compression algorithm is not executed, as shown in FIG. 2;
in a particular embodiment of the present invention,
point cloud data discrete abnormal point elimination, wherein in the laser radar operation scanning process, the influence of light, water vapor interference and environmental factors is received, in the laser radar operation scanning process, discrete points deviating from the whole and abnormal points exceeding normal values are generated, and the discrete abnormal points are eliminated by using difference values, as shown in fig. 3;
in a particular embodiment of the present invention,
extracting feature points, wherein in the data compression process, if the error value of a compression algorithm is increased due to the loss of the feature points, judgment is carried out according to the point location data values in the grids, and if the maximum and minimum difference values in the grids are greater than the height value, the feature points exist in the grids, and feature extraction is carried out, as shown in fig. 4;
in a particular embodiment of the present invention,
point cloud compression, which is to compress the divided grids in a certain proportion, and to add the extracted feature points into the compressed point cloud data, and further comprises: when point cloud data is compressed, compression is performed according to a certain proportion according to a divided grid, if 100 point locations exist in the grid, the compression ratio is 10, the number of remaining point locations after compression is 10, the point cloud data is uniformly compressed, after the point cloud data is compressed, extracted feature points are added into the compressed point cloud data, and the point cloud data is stored to generate serialized point cloud data, as shown in fig. 5.
In order to facilitate understanding of the above-described technical aspects of the present invention, the above-described technical aspects of the present invention will be described in detail below in terms of specific usage.
When the method is used specifically, according to the method for compressing the three-dimensional space measurement result data, the laser radar transmits laser pulses through self rotation to realize 360-degree imaging, the number of turns of the laser radar rotating around the laser radar per second is called as frame frequency, the distance position and angle fixed value of the laser emitted by the laser radar in each rotation period are not changed along with the rotation number of the laser radar, namely the frequency of the laser pulses emitted by the laser radar is a fixed value, the angle position of each time of the laser radar transmitting can be used for calibrating the laser radar point cloud data, the line number of the laser radar is the number of the laser pulses emitted longitudinally and simultaneously in space, the three-dimensional point cloud data of a target profile after the laser radar irradiates a target is projected on an XY plane to present a trend of large overlapping degree, the laser point clouds in the area have large density and the distance between the point clouds is closer, therefore, target identification can be effectively carried out by compressing the three-dimensional point cloud data, the time for searching the target in the three-dimensional space is reduced, and the identification efficiency of the target is greatly improved;
the method is technically characterized in that the controllable compression efficiency of the point cloud data is realized, the compression ratio is used as an optional parameter to be transmitted in the algorithm, so the compression efficiency is controlled, although the higher the compression efficiency is, the better the compression efficiency is, the higher the compression efficiency is, the more the lost information point positions are, so a balanced compression point can be found for each point cloud by using the controllable compression efficiency, the point positions are serialized after the point cloud compression, the compressed point cloud data should present an ordered point cloud, thereby being beneficial to converting the point cloud into a three-dimensional graph for outputting in the later period, the point cloud data reduces the error value, the method can also reserve the characteristic points in the area while controlling the compression ratio, and eliminate the discrete points and abnormal points, thereby ensuring the compression efficiency and ensuring that the overall characteristic of the point cloud is not changed greatly, the error value is ensured to be reduced;
the definitions of the terms appearing in the present invention are given below:
data compression: on the premise of not losing useful information, the data size is reduced to reduce the storage space, or the data is reorganized to reduce the data redundancy;
compression ratio: the compression ratio of the size of the file after compression to the size of the file before compression is generally smaller and better;
error value: which refers to the variation in the precision of the compressed data relative to the original data.
In summary, according to the above technical solution of the present invention, point cloud data compression is performed by acquiring point cloud data, and the core steps include: the method comprises the following steps of uniform meshing division of point cloud data, elimination of point cloud data discrete points and abnormal points, extraction of point cloud data characteristic points, point cloud compression and data splicing.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. A compression method for three-dimensional space measurement result data is characterized by comprising the following steps:
s1: acquiring point cloud data, namely scanning an object by using laser radar equipment to obtain initial data, and converting the initial data by using a trigonometric function to obtain rectangular coordinate system data;
s2: carrying out uniform grid division on the point cloud data, carrying out proportional division on the point cloud data according to the transmitted parameters, and adopting a uniform division mode for the division of the point cloud data;
s3: point cloud data discrete abnormal points are removed, discrete points deviating from the whole body and abnormal points exceeding normal values are generated in the running scanning process of the laser radar, and discrete abnormal points are removed by using difference values;
s4: extracting characteristic points, namely judging according to point location data values in the grids, and if the maximum and minimum difference values in the grids are larger than the height value, indicating that the characteristic points exist in the grids;
s5: and point cloud compression, namely compressing the points in a certain proportion according to the divided grids, and adding the extracted feature points into the compressed point cloud data.
2. The method for compressing data of measurement results in three-dimensional space according to claim 1, wherein S1.1 obtains point cloud data of three-dimensional lidar, and performs function conversion on data of angle and distance of transmission of the lidar.
3. The method according to claim 1, wherein S2.1 is divided evenly according to the parameters, and if the parameter value is smaller than the largest rectangular coordinate value and not equal to zero, the compression algorithm is executed, and if the parameter value is equal to the rectangular coordinate value, the compression algorithm is not executed.
4. The method for compressing the data of the measurement result of the three-dimensional space as claimed in claim 1, wherein in the operation scanning process of the S3.1 laser radar, the discrete points deviated from the whole and the abnormal points exceeding the normal value are eliminated by the difference value under the influence of light, water vapor interference and environmental factors.
5. The method for compressing data of measurement results in three-dimensional space according to claim 1, wherein in the S4.1 data compression process, if the feature point is lost, the error value of the compression algorithm is increased, the judgment is performed according to the data value of the point location in the grid, and if the maximum and minimum difference values in the grid are greater than the height value, the feature point exists in the grid, and the feature extraction is performed.
6. The method for compressing the three-dimensional space measurement result data according to claim 1, wherein in S5.1, the point cloud data is compressed according to a certain proportion by a divided grid, if 100 point locations exist in the grid and the compression ratio is 10, the remaining point locations after compression are 10, and the point cloud data is uniformly compressed;
s5.2, after the point cloud data is compressed, adding the extracted feature points into the compressed point cloud data, and storing the point cloud data to generate serialized point cloud data.
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| CN112415490A (en) * | 2021-01-25 | 2021-02-26 | 天津卡雷尔机器人技术有限公司 | 3D point cloud scanning device based on 2D laser radar and registration algorithm |
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| CN116758174A (en) * | 2023-08-16 | 2023-09-15 | 北京易控智驾科技有限公司 | Compression transmission method and device for laser point cloud data, electronic equipment and storage medium |
| CN116758174B (en) * | 2023-08-16 | 2023-11-10 | 北京易控智驾科技有限公司 | Compression transmission method and device for laser radar point cloud data and electronic equipment |
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