Point cloud automatic registration method based on a local feature descriptor

Abstract

The invention provides a point cloud automatic registration method based on a local feature descriptor. an initial matching point pair can be obtained based on a local fast point feature histogram descriptor and a sampling consistency algorithm; A coarse registration matrix is obtained through an error measurement loss function, and then a fine registration rigid body transformation matrix is obtained by combining an iteration nearest point algorithm, so that the problem that three-dimensional point clouds cannot be automatically registered under different visual angles can be effectively solved; Compared with an existing method, the method has the advantages that mark points do not need to be pasted, the method is not affected by a positioning device, an additional auxiliary device is notneeded, the requirement for the environment is not high, efficiency is high, and the method has high robustness in actual measurement.

Description

technical field

[0001] The invention relates to the field of point cloud data registration, in particular to an automatic point cloud registration method based on local feature descriptors. Background technique

[0002] With the development of three-dimensional measurement technology, surface structured light three-dimensional measurement method, as a non-contact optical measurement method with fast measurement speed and high precision, is widely used in industrial production detection parts, biomedical and historical relic protection, product quality Control, model digitalization and other fields. In the actual measurement process, due to the occlusion of the measured object itself and the environment, or some limitations in the measurement range, a single measurement can only obtain 3D data on a part of the surface, so it is necessary to measure more from different perspectives and poses. Measure the measured object once to obtain a complete 3D data model. Among them, th...

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