Point cloud registration method based on semantic annotation results for complex structures of ship hull segments

A point cloud registration method based on semantic annotation results for complex structures of ship hull segments, comprising the following steps:

• I. coarse registration algorithm based on salient structures:
• (1) automatic coarse registration algorithm:
• extending a coarse registration method for constraining degrees of freedom of a spatial rigid body, and also using the salient members for constraining the degrees of freedom of the spatial rigid body;
• simultaneously calculating out fully constrained combinations of all identifiable members and recording them as a constraint combination table T;
• for a salient member group, arranging the members according to member category labels and the aforementioned constraint combination table T to form all feasible coarse registration constraint sets, and finally calculating out an optimal coarse registration result based on an RANSAC principle;
• (2) manual coarse registration algorithm:
• for segmented and annotated original point clouds, completing manual selection of the salient members according to the constraint combination table T in the above automatic coarse registration algorithm, and thus completing coarse registration;
• and
• II. after choosing either the automatic coarse registration algorithm or the manual coarse registration algorithm, performing a point cloud fine registration algorithm based on probability correspondence:
• (1) for simplified point clouds, utilizing a point correspondence probability function fc, and giving a local correspondence probability matrix between points to be considered and their neighboring CAD points, lines, and surfaces according to a Gaussian function and spatial grid subdivision;
• (2) based on a CPD probability matrix and a point cloud-CAD target optimization formula, formulating an optimization function considering the correspondence probability matrix in the form of formula I, and completing optimization equation solving based on a quaternion derivation method from Horn et al.;
• finally, improving an ICP algorithm with optimization equation solving results, taking the simplified point clouds and a CAD model as registration objects, making each iteration to traverse each point in the point clouds, calculating a local probability correspondence matrix, assembling a calculated local probability correspondence matrix into a global probability matrix, further solving an optimization objective function, and gradually approaching a local optimal solution, wherein:
• the formula I is as follows:
• min
• R
• ,
• t
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• n
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• 1
• N
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• m
• =
• 1
• M
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• P
• nm
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• Rx
• n
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• N:
• number of points in a moving point cloud;
• M:
• number of points in a fixed point cloud;
• Pnm:
• element in the n-th row and m-th column of the correspondence probability matrix;
• R:
• rotation matrix to be solved;
• t:
• translation matrix to be solved;
• xn:
• n-th point in the moving point cloud;
• and ym:
• m-th point in the fixed point cloud.