CN-121600362-B - Data fusion method and electronic equipment

CN121600362BCN 121600362 BCN121600362 BCN 121600362BCN-121600362-B

Abstract

The application provides a data fusion method and electronic equipment, which comprise the following steps of obtaining a first data set and a second data set, generating a third data set by adopting a linear interpolation method on the first data set, determining an elevation difference data set according to the second data set and the third data set, determining weights of all third data in the third data set according to the elevation difference data set, determining elevation difference values of all third data according to the weights of all third data and the elevation difference data set, and determining a fourth data set according to the elevation difference values of all third data and the third data set, wherein the fourth data set is a set formed by high-precision high-density data. According to the scheme provided by the application, the low-precision and high-density point cloud data and the high-precision and low-density point cloud data are fused, so that the precision of the low-precision point cloud data is improved, the high-density and high-precision point cloud data is formed, and the fusion of the multi-source data is realized.

Inventors

WANG HUAILI
ZHANG XINGQIANG
LIU HONG
LUO AOMEI
YIN XUEWEI
YANG JINGDAO

Assignees

中交上海航道局有限公司

Dates

Publication Date: 20260512
Application Date: 20260129

Claims (7)

1. A method of data fusion comprising the steps of: acquiring a first data set and a second data set, wherein the first data set is a set formed by low-precision high-density data, the second data set is a set formed by high-precision low-density data, and the first data set and the second data set are both point cloud data; generating a third data set by adopting a linear interpolation method on the first data set; determining an elevation difference data set according to the second data set and the third data set; Determining the weight of each third data in the third data set according to the elevation difference data set; Determining the elevation difference value of each third data according to the weight of each third data and the elevation difference data set; determining a fourth data set according to the elevation difference value of each third data and the third data set, wherein the fourth data set is a set formed by high-precision high-density data; The low-precision high-density data are obtained through unmanned aerial vehicle laser radar or unmanned aerial vehicle aerial photogrammetry; the high-precision low-density data are obtained through real-time dynamic differential positioning or total station measurement; the determining the weight of each third data in the third data set according to the elevation difference data set comprises the following steps: constructing a triangular network according to the elevation difference data set; Constructing a first polygon according to the triangular net; Inserting each third data point by point into the first polygon to construct a second polygon; And determining the weight of each third data according to the overlapping area of the first polygon and the second polygon.
2. The method of claim 1, wherein generating a third data set by linear interpolation of the first data set comprises: And generating a third data set by adopting a bilinear interpolation method or a Kriging interpolation method on the first data set, wherein the third data set is a regular grid data set, and the grid spacing is determined according to a measurement scale.
3. The method of claim 1, wherein determining an elevation difference dataset from the second dataset and the third dataset comprises: searching each third data corresponding to each second data in the second data set in the third data set; Subtracting the elevation value of the third data from the elevation value of the second data for each group of corresponding second data and third data to obtain an elevation difference value of the second data; and determining the elevation difference data set according to the elevation difference value of each second data.
4. The method of claim 1, wherein constructing a triangle network from the elevation difference dataset comprises: constructing a triangle by using the points in the elevation difference data set, wherein the interior of a circumscribed circle of the triangle does not contain the points in the elevation difference data set; and combining the triangles to construct the triangular net.
5. The method of data fusion according to claim 1, wherein constructing a first polygon from the triangle mesh comprises: If the edge in the triangular net is shared by two triangles in the triangular net, taking a connecting line of the circle centers of the circumscribed circles of the two triangles as the inner edge of the first polygon; if the edge in the triangular net is used by only one triangle in the triangular net, taking the perpendicular bisector from the circle center of the circumscribed circle of the triangle to the edge as the boundary edge of the first polygon; and constructing the first polygon according to the inner side and the boundary side.
6. The method of claim 1, wherein determining the elevation difference value of each third data according to the weight and the elevation difference data set of each third data comprises: Determining the elevation difference value of each second data in the second data set according to the elevation difference data set; And multiplying the weight of the third data by the elevation difference value of the corresponding second data for each third data, and then summing to obtain the elevation difference value of the third data.
7. An electronic device, the electronic device comprising: A processor; A memory for storing processor-executable instructions; Wherein the processor is configured to perform the data fusion method of any of claims 1-6.

Description

Data fusion method and electronic equipment Technical Field The present invention relates to the field of mapping, and in particular, to a data fusion method and an electronic device. Background The three-dimensional laser scanning technology is widely applied due to the unique technical advantages of non-contact measurement, high efficiency, high density and the like, and is generally combined with synchronous operation of other sensors to acquire three-dimensional point cloud data of the earth surface, and is mainly divided into an airborne type, a vehicle-mounted type, a handheld type, a standing type and the like. For example, unmanned plane laser radar measurement is to carry laser radar sensors through unmanned plane, and acquire point cloud data by combining a Global Navigation Satellite System (GNSS) and an Inertial Measurement Unit (IMU). The height measurement precision is about 10cm-15cm under the influence of instrument, environment and personnel operation. Similarly, the unmanned aerial vehicle aerial photogrammetry rapidly acquires the ground topography condition through a digital camera, the elevation measurement precision is similar to that of a laser radar, and compared with the traditional total station and real-time dynamic differential positioning (RTK) measurement technology, the unmanned aerial vehicle aerial photogrammetry has the advantages of high efficiency, full coverage, large data volume, lower precision and incapability of achieving the traditional measurement precision of 1cm-3cm, and the traditional technology cannot rapidly acquire large-area high-density point cloud data. Therefore, in actual operation, a plurality of measurement techniques are often adopted at the same time, but the accuracy and density of the obtained measurement data are greatly different, and the fusion processing and the utilization are difficult. Regarding data fusion, the problems of incapability of guaranteeing fusion quality, low overall result quality caused by low-precision data by using an average method, uncertainty propagation, pollution of fusion results caused by the uncertainty of the low-precision data, and difficulty in weight distribution are faced, namely, how to scientifically give higher weight to the high-precision data, and meanwhile, the value of the low-precision data is not thoroughly abandoned. Therefore, a data fusion method is needed to effectively overcome the above problems, fuse multi-source data (i.e. data with different precision and density collected by different sensors), improve the quality of low-precision data, and ensure the effect of data fusion. Disclosure of Invention The embodiment of the application provides a data fusion method and electronic equipment, which solve the problems that multi-source data are difficult to fuse and the quality of the fused data cannot be guaranteed. The embodiment of the application provides a data fusion method which comprises the steps of obtaining a first data set and a second data set, wherein the first data set is a set formed by low-precision high-density data, the second data set is a set formed by high-precision low-density data, a third data set is generated by adopting a linear interpolation method on the first data set, a height difference data set is determined according to the second data set and the third data set, the weight of each third data in the third data set is determined according to the height difference data set, the height difference value of each third data is determined according to the weight of each third data and the height difference data set, and a fourth data set is determined according to the height difference value of each third data and the third data set, wherein the fourth data set is a set formed by high-precision high-density data. In an embodiment, the low-precision high-density data is obtained by unmanned aerial vehicle lidar or unmanned aerial vehicle aerial photogrammetry. In one embodiment, the high-precision low-density data is obtained by real-time dynamic differential positioning or total station measurement. In one embodiment, the generating a third data set by linear interpolation on the first data set includes generating a third data set by bilinear interpolation or kriging interpolation on the first data set, wherein the third data set is a regular grid data set, and the grid distance is determined according to a measurement scale. In one embodiment, determining the elevation difference data set according to the second data set and the third data set comprises searching third data corresponding to second data in the second data set in the third data set, subtracting the elevation value of the third data from the elevation value of the second data to obtain the elevation difference value of the second data according to the elevation value of the third data for each group of corresponding second data and third data, and determining the elevation difference data set according to the