Point cloud precise registering method based on gravity center Euclidean distance

Abstract

The invention discloses a point cloud precise registering method based on a gravity center Euclidean distance. By calculating the gravity center of an effective point set of a point cloud overlaying portion and the Euclidean distance of all points in each point set relative to the gravity center, optimization screening is conducted, and overall identical points are solved. By judging a hazard ball, the stable overall identical points are obtained, conversion parameters among point cloud coordinate systems are obtained, and precise registering of the point cloud of two measuring stations is achieved. The method adopts the gravity center Euclidean distance to register the point cloud, due to the fact that the gravity center Euclidean distance has the characteristics of uniqueness and quantity limit and the like, point cloud registering speed and precision are improved. By judging the hazard ball, an equal Euclidean distance distinguishing problem is solved, and point cloud matching obscureness is avoided. Point gaps are used as error margins for judging identical Euclidean distance, and registering is more precise. By removing error points in the point cloud continuously, a purpose that abnormal value disturbance is not affected is achieved. A step by step process of first rough registering and then precise registering enables point cloud registering to be easy.

Description

technical field [0001] The invention relates to the fields of computer graphics, surveying and mapping, reverse engineering technology and the like, in particular to the problem of multi-site cloud registration, in particular to a point cloud precise registration method based on the Euclidean distance of the center of gravity. Background technique [0002] In the data acquisition stage of the 3D reconstruction of the object, it is necessary to use a 3D laser scanner to scan the object in all directions. However, due to the complexity of the object, it is impossible to scan all the point cloud data of the object at one time. Substation scanning is required, which is faced with The problem of multi-site cloud registration. The registration of multi-site clouds is the process of planning all the station cloud coordinate systems to the same common coordinate system. Multi-site cloud registration can be transformed into a pairwise registration problem between two adjacent statio...

AI Technical Summary

Problems solved by technology

The public point method requires at least 3 pairs of artificial signs or feature points between two adjacent stations, and the least square method is used to solve the conversion parameters. This method has high precision and reliability, but this method needs to scan each sign separately and identify the center, and record the point number Registration is a semi-automatic method; the ICP (Iterative Closest Point) algorithm is a well-known algorithm proposed in the 1990s. This algorithm first assumes an initial attitude transformation parameter, selects a certain number of points from a site cloud, and Select the adjacent points of these points in another point cloud as the corresponding points, and iteratively calculate the distance between point pairs until the convergence condition is met. However, the ICP algorithm is susceptible to outliers in practical applications. Insufficient such as slow operation speed

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