An automatic fusion method for multi-terminal equipment to obtain 3D point cloud

Abstract

The invention discloses an automatic fusion method for obtaining three-dimensional point clouds by multi-terminal equipment, which is used to solve the difficult problem of automatic fusion caused by different scaling coefficients between three-dimensional point clouds obtained by different terminal devices. The present invention provides a multi-step point cloud scaling coefficient calculation method from coarse to fine, which can calculate relative scaling coefficients between different source point clouds, improves the fusion efficiency of point clouds obtained by different devices, and satisfies the requirements of different devices in specific scenarios. Point cloud complementary requirements. The key point of this method is that it does not need to limit the way to obtain the point cloud, and it has the advantages of strong versatility and high degree of automation.

Description

technical field [0001] The invention relates to a three-dimensional point cloud fusion method, and an automatic fusion method for obtaining three-dimensional point clouds by a multi-terminal device. Background technique [0002] Three-dimensional imaging technology has important applications in today's industry, national defense and other fields. Current 3D technologies include but are not limited to stereo vision imaging, multi-view imaging, oblique photography, structured light 3D imaging, lidar 3D imaging and other 3D imaging technologies. The key to 3D imaging technology is to obtain the depth information of objects or scenes or the corresponding 3D point cloud data to provide the machine with depth perception capabilities or 3D spatial data models. The above three-dimensional imaging technology has been developed in recent years and has become mature, and has considerable applications in entertainment, industrial inspection, national defense and other fields. However,...

AI Technical Summary

Problems solved by technology

However, in some application scenarios, neither the homogeneous point cloud nor the continuity of map collection can be guaranteed, and the difficulty of point cloud fusion in this scenario increases sharply.

One of the bottlenecks in the fusion between point clouds from different sources is that the scaling factor between point clouds may not be predictable, or due to different calibration methods and imaging principles, it is difficult to ensure a high degree of consistency between point cloud data obtained by different imaging technologies scaling ratio

In addition, because the map acquisition process of different methods may be discontinuous, or the coordinate system of the same setting is different, the relative spatial positions between different point clouds cannot be obtained in advance, which makes it impossible to directly estimate the zoom factor through direct zooming combined with fine registration methods

[0004] In summary, it can be found that a key problem in the fusion of point clouds from different sources is that the scaling factor between point clouds is difficult to evaluate

Existing commercial software provides quite a lot of point cloud operation tools, including point cloud scaling factor evaluation and point cloud fusion, but these software require more manual intervention to ensure better processing results

This is extremely inefficient or even inapplicable for application scenarios that require fusion of a large number of different source point clouds

Images