A point-cloud registration method that integrates shape and texture features is proposed to address the issues of unsatisfactory registration performance and low accuracy in existing point-cloud registration algorithms when the geometric features of the point cloud are insignificant.First,keypoints with geometric and texture features change significantly on the surface of a point cloud are extracted,the shape and texture of the keypoints are characterized,and key-point matching is performed based on feature similarity.Subsequently,a random-sampling consensus algorithm is used to eliminate mismatched points and estimate the pose matrix,thus achieving coarse registration and providing favorable initial pose values for the subsequent fine registration.Finally,a color iterative closest point(ICP)registration algorithm is used for fine registration.Experimental results show that this algorithm offers high registration accuracy when used for color point-cloud models with clutter,low overlap rates,and insignificant shape features.
point cloud registrationcolor point-cloudpoint cloud keypointslocal feature descriptorsfeature fusion
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