Multi-platform point cloud matching method based on surface features

Abstract

The invention discloses a multi-platform point cloud matching method based on surface features, and belongs to the technical field of mobile measurement point cloud matching. The method comprises thefollowing implementation steps: acquiring point cloud data of a target object, performing data denoising preprocessing, and converting a preprocessed original point cloud coordinate system into a local coordinate system which takes a gravity center as an origin of coordinates and has an invariable coordinate axis direction; uniformly selecting three pairs or more than three pairs of matching planes from the acquired point cloud data, performing plane fitting by adopting a robust RANSAC algorithm to extract plane parameters (A, B, C and D) of a common plane, and performing flatness evaluation on the plane point cloud; based on the plane parameters of the three pairs or more than three pairs of corresponding planes, converting the rotation parameters into a non-linear model, then performinglinearization processing, keeping the translation parameters unchanged, and performing parameter solving by adopting an overall least square method; evaluating the correlation of the to-be-registeredplanes based on the correction number of the adjustment result, and reselecting the planes which do not meet the requirements; and completing point cloud matching based on the coordinate conversion model.

Description

technical field

[0001] The invention discloses a multi-platform point cloud matching method based on surface features, and belongs to the technical field of mobile measurement point cloud matching. Background technique

[0002] At present, vehicle-mounted, airborne, and single-station LiDAR scanners are affected by the attitude of the platform, the accuracy of control points, the accuracy of GPS, and the accuracy of inertial navigation. There are slight deviations between the multi-source data coordinate systems collected. The acquisition methods of each of the above platforms have certain applicable conditions. In actual production applications, point cloud data from multiple platforms need to be matched and fused to obtain multi-dimensional, multi-temporal point cloud data of the target object. In order to make full use of the information in the multi-source data, it is necessary to match the coordinate system of the multi-source point cloud data and complete the splicing ...

Images