Abstract
Abstract
This paper presents a novel data processing algorithm. This
algorithm is used to solve the problem of incomplete and misaligned
of point cloud data due to the complexity of nuclear power
containment cone-cylinder forgings and the limitation of laser
scanner. Based on spectral graph theory and Hungarian matching, this
paper first introduces the lazy random walk, and point cloud state
vector is calculated during the walk to judge the local
information, thereby eliminate the influence of noise. Then,
characteristic edges are extracted using spectral graph
theory. Additionally, the feature descriptors are calculated and the
cost matrix is constructed using the feature descriptors. The
Hungarian algorithm is applied for feature matching, facilitating a
coarse registration of the point clouds. Finally, the improved
point-to-plane iteration closest point is used for fine registration
to ensure accurate alignment between point clouds. The experimental
results demonstrate the algorithm's effectiveness in the
registration of point clouds for nuclear power containment
cone-cylinder forgings.