  Matching 3D rigid point clouds in complex environments robustly and
accurately is still a core technique used in many applications. This paper
proposes a new architecture combining error estimation from sample covariances
and dual global probability alignment based on the convolution of adaptive
Gaussian Mixture Models (GMM) from point clouds. Firstly, a novel adaptive GMM
is defined using probability distributions from the corresponding points. Then
rigid point cloud alignment is performed by maximizing the global probability
from the convolution of dual adaptive GMMs in the whole 2D or 3D space, which
can be efficiently optimized and has a large zone of accurate convergence.
Thousands of trials have been conducted on 200 models from public 2D and 3D
datasets to demonstrate superior robustness and accuracy in complex
environments with unpredictable noise, outliers, occlusion, initial rotation,
shape and missing points.
