An improved ICP object point cloud splicing method for fusing fast point characteristic histogram

Abstract

The invention discloses an improved ICP object point cloud splicing method for fusing fast point characteristic histogram. The method comprises the steps of projecting a standard sinusoidal digital grating onto the surface of the object to be measured, photographing stripe images of the surface of the object projected with the standard sinusoidal digital grating from different angles of view by aCCD camera, and obtaining photographing point clouds from multiple angles of view; for two image point clouds that need to be stitched together, building a k-D tree and interpolate to obtain that interpolated point cloud; for the two interpolated point clouds to be spliced, computing the fast point feature histogram, and obtaining the point cloud by random sampling consistent transformation; usingthe improved iterative nearest point method to obtain the first interpolated point cloud which is precisely registered; overlaying point cloud and mesh to realize the mosaic of two different angles of view of the shooting point cloud. The invention has low requirement for the initial position of the splice point cloud, the robustness is remarkably improved, the local optimization is not easy to fall into, the splice accuracy is improved, and the precise splicing of the point cloud under multi-view angles is realized, so that the practical industrial application requirements can be met.

Description

technical field [0001] The invention relates to a point cloud processing method and system, in particular to an improved iterative closest point (ICP) point cloud splicing method for fusing fast point feature histograms. Background technique [0002] Multi-view point cloud stitching is an important part of reconstructing object surface data. This technology has always been a research hotspot and difficulty in the fields of reverse engineering, computer vision, surface quality inspection, and photogrammetry. The core of point cloud stitching is to find the coordinate transformation parameters R (rotation matrix) and T (translation matrix), so that the distance between the three-dimensional data measured from two perspectives after coordinate transformation is the smallest. [0003] At present, there are mainly two types of point cloud stitching methods. Automatic splicing on the basis of manual assistance, use the motion positioning device to calculate the point cloud rotati...

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Problems solved by technology

[0005] In order to solve the problems of low splicing accuracy, insufficient stability, and low splicing efficiency in the point cloud splicing process, the present invention proposes an improved iterative closest point (ICP) point cloud splicing method that fuses fast point feature histograms. Improve on the basis of point iteration, use the k-d tree search method, combine the Euclidean distance of the point cloud and the direction angle threshold to remove the wrong point pair, and realize the efficient and accurate splicing of the point cloud

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