A point cloud misregistration filtering method and system for three-dimensional measurement of a complex special-shaped curved surface robot

Abstract

The invention discloses a point cloud misregistration filtering method and system for three-dimensional measurement of a complex special-shaped curved surface robot, and the method comprises the following steps: 1, dividing input registration point cloud pairs into three groups, selecting one point cloud pair from each group of registration point cloud pairs, and solving a corresponding transformation matrix Ri; 2, converting the obtained transformation matrix into an Euler rotation angle, and constructing an Euler rotation angle set D; Step 3, performing self-adaptive density clustering on the Euler rotation angles in the step D, selecting a class with the clustering result containing the maximum number of the Euler rotation angles, and taking a registration point cloud pair for solving the Euler rotation angles as a credible registration point cloud pair so as to complete error registration point cloud pair filtering; according to the method, spatial information of a given point cloud registration pair is fully utilized, effective decomposition and efficient clustering are carried out, so that false registration points in three-dimensional point cloud registration can be effectively eliminated, and the method has relatively high adaptability to noise of point cloud, registration position deviation and the like. The system is simple in structure and convenient to operate.

Description

technical field [0001] The invention belongs to the field of image registration, and in particular relates to a point cloud misregistration filtering method and system for three-dimensional measurement of complex special-shaped curved surface robots. Background technique [0002] Intelligent manufacturing technology is to replace human beings to complete high-precision, high-intensity repetitive operations through the integration of intelligent perception, human-computer interaction, decision-making and execution technologies through industrial robots. It can complete complex workpiece identification, positioning and grasping, welding and grinding, drilling and riveting and other tasks. [0003] Intelligent manufacturing is developing from 2D machine vision to 3D machine vision, but compared with 2D machine vision, the accuracy of point cloud registration in 3D visual space has always been lower. The main reason is that the three-dimensional space has high data complexity, ...

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Problems solved by technology

However, both methods have uncontrollable misjudgments and require certain prior knowledge to operate accurately.

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