Clustering method for building laser scan point cloud data

Abstract

The invention discloses a clustering method for building laser scan point cloud data. The method comprises the following steps: converting the point cloud data into two-dimensional data to acquire a set X; appointing density threshold value MinPts; calculating the longest distance of MinPts objects with shortest distance from the point towards any point in the X; counting maximal and minimum value of the longest distance of all the points; classifying the difference value of the maximal and minimum value of the longest distance into n equal portions; making a circle by adopting the point generating the minimum value of the longest distance as the center and the gradual increasing value of the distance of the equal portions as the radius; calculating the number of points in each circle; adopting the difference value as a abscissa; adopting the numbers of the points in each circle as an ordinate; conducting fitting to form a curve; seeking points of inflexion of the curve; taking the number value of the abscissa corresponding to the points of inflexion as the value of sigma; building AQ-DBSCAN algorithm by adopting the MInPts and the sigma as conditions; conducting clustering on the points in the X to obtain the belonged cluster analysis of the points in the point cloud in the parts of the building.

Description

technical field [0001] The invention relates to a clustering method for building laser scanning point cloud data. Background technique [0002] Ancient architecture is an important symbol of human civilization and a special carrier of cultural information. Therefore, inheriting and protecting ancient buildings is an unshirkable responsibility of contemporary people. However, due to the current economic development and urban construction, as well as the erosion of ancient buildings over time, many large ancient buildings have been deformed and damaged to varying degrees, and the protection of ancient buildings is facing a severe situation. [0003] Three-dimensional color scanning technology has the characteristics of rapidity, non-contact, penetration, real-time, dynamic, initiative, high density, high precision, digitization, automation, etc. With the improvement of its measurement accuracy, scanning speed, spatial resolution, etc. With progress and price reduction, it ha...

AI Technical Summary

Problems solved by technology

Aiming at the characteristics of ancient building point cloud data, the present invention proposes an improved AQ-DBSCAN algorithm based on the DBSCAN algorithm, and provides a clustering method for building laser scanning point cloud data using the AQ-DBSCAN algorithm. The AQ-DBSCAN algorithm of the present invention The DBSCAN algorithm solves the problem of automatic estimation of the neighborhood radius in the DBSCAN algorithm, and the problem of effectively selecting representative points, that is, reducing the amount of calculation when spreading in the cluster, so as to quickly perform density clustering processing.

Images