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.