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

Robust Rigid Point Registration based on Convolution of Adaptive Gaussian Mixture Models