Point cloud smooth fairing filtering method based on normal vector

Abstract

The invention discloses a point cloud smooth fairing filtering method based on a normal vector. The method comprises steps of firstly, removing outliers of an original point cloud; carrying out principal component analysis on the discrete point cloud set to deduce the normal vector of each point, and adjusting the normal vectors of all the points to be in the same direction; fitting a plane for each point by using a least square method through K neighbors of the point, obtaining a normal vector of the point through a plane model, and adjusting the normal vector to be in the same direction; correcting the normal vector calculated in the first step by utilizing the normal vector of the fitting plane; and finally, projecting the point to the fitting plane along the direction of the correctednormal vector, i.e., adjusting the position of the point to the intersection position of the corrected normal vector and the fitting plane so as to achieve an effect of carrying out smooth fairing processing on the point cloud. The method is easy to implement, the smooth fairing effect is obvious, the original point cloud is filtered to lay a foundation for triangularization of subsequent point cloud data and generation of DEM, and the method is suitable for fairing and smoothing the original point cloud data and is high in practical value.

Description

technical field [0001] The invention relates to the field of laser Lidar scanning surveying and mapping, in particular to a point cloud smoothing and smoothing filtering method based on normal vectors. Background technique [0002] With the rapid development of laser technology and computer technology, airborne laser measurement has become a new technology to efficiently obtain high-precision and reliable 3D data. [0003] It integrates advanced technologies such as high-precision dynamic GPS differential positioning, inertial navigation, and laser ranging. This technology can quickly, accurately and contactlessly acquire 3D point cloud information on the surface of complex objects, and then complete the 3D reconstruction of the entity. It has been widely used in digital cities, topographic mapping, geographic information systems, medical engineering, cultural relics protection, robots Navigation and other industries. [0004] However, due to the influence of the physical ...

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Problems solved by technology

[0004] However, due to the influence of the physical characteristics of the scanning equipment, scanning environment, system error and integration error, the obtained original point cloud data is often polluted by noise, and the effect of directly triangulating and generating DEM with the original point cloud data is very poor. It is necessary to perform smoothing and smoothing filtering on the point cloud, but the existing methods cannot achieve a good smoothing and filtering effect

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