Point cloud registration method and system based on feature extraction module and dual quaternion

Abstract

The invention discloses a point cloud registration method and system based on a feature extraction module and dual quaternion, and the method comprises the steps: reading 3D point cloud data from a data set, and obtaining the three-dimensional coordinates and category labels of points contained in each sample; randomly sampling points contained in each sample to obtain a template point cloud; translating and rotating the template point cloud to obtain a source point cloud, and dividing the source point cloud and the template point cloud into a training set and a test set according to the category label of the sample; constructing a point cloud registration model based on the feature extraction module and dual quaternion, and constructing a loss function of the point cloud registration model; and training a point cloud registration model by using the divided training set, and performing point cloud registration on the divided test set by using the trained point cloud registration model. According to the method, global and local information in the point cloud is fully mined, and the deficiency of local features in a global feature extraction stage of point cloud registration is effectively made up.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a point cloud registration method and system based on a feature extraction module and a dual quaternion. Background technique [0002] With the rapid development of 3D data acquisition technology, point cloud data collected by lidar, structured light sensors and stereo cameras have been widely used. As such, this has drawn considerable attention when developing algorithms for direct point cloud registration, classification, segmentation, tracking, etc. 3D rigid body point cloud registration is a key task in many important applications in computer vision and robotics, including autonomous driving, surgical navigation, and simultaneous localization and mapping (SLAM). The goal of point cloud registration is to find a rigid transformation that aligns one point cloud with another. However, inherent structural flaws make it very difficult to directly use point c...

AI Technical Summary

Problems solved by technology

[0004] However, based on the recently proposed feature extraction without corresponding methods, the usual process is to extract the global features of the point cloud through the point network, and then perform a series of transformations on the global features to obtain the final registration result; however, using the point network One of the biggest disadvantages is that these forget not to consider the local characteristics of the point cloud, and do not make full use of the point cloud information

Therefore, the extracted global features will have a lot of point information missing.

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