A method suitable for multi-view automatic registration of multi-station ground laser point cloud data

Abstract

The invention relates to a method suitable for multi-view-angle automatic global registration of ground laser point cloud data. The method involves two key modules: a module for extracting semantic feature points and a module for feature matching. The method comprises: step 1, extracting the semantic feature points: obtaining the semantic feature points in a series of modes of data slicing, distance clustering, geometric element fitting and the like; step 2, matching the semantic feature points: matching the semantic feature points by constructing a triangular geometric constraint condition and a semantic constraint condition, and removing error matching in a geometric consistency clustering mode; and finally, constructing a weighted undirected graph by taking a reciprocal of a feature point number as a weight, and finally obtaining global registration parameters of all stations by taking a minimum spanning tree of the weighted undirected graph as a registration path, thereby realizing global optimal registration. According to the invention, the method suitable for multi-view-angle automatic global registration of the ground laser point cloud data is constructed; the method can effectively resist influences of noise, point density and coverage; and the method improves the laser scanning operation efficiency, thereby having very high practical values.

Description

technical field [0001] The invention relates to multi-station automatic registration of laser scanning point clouds of ground stations, and belongs to the field of automatic research of laser point cloud measurement data processing. Background technique [0002] With the emergence and development of laser scanning technology, people can quickly obtain dense surface point cloud data of objects and scenes. This technology is widely used in reverse engineering, virtual reality, 3D reconstruction and other fields. Due to the limited range and distance of each scan, multiple station scans are required to obtain the complete point cloud data of a scene or object. The coordinates obtained by each station scan are relative to the local coordinates of the scan center, which requires the use of registration technology to unify the data between different stations into the same coordinate system. The current mainstream registration is generally divided into two steps: coarse registrat...

AI Technical Summary

Problems solved by technology

In scenarios where these algorithms fail, registration needs to be done manually, which greatly increases the manual workload and reduces production efficiency

On the other hand, the above algorithms only involve pairwise registration, and need to manually specify the positions of two adjacent stations, which cannot achieve global automatic registration

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