Acoustic perception in underwater environments is challenging due to the low frequency of the acquisition system and multiple and huge sources of noise. Therefore, point clouds built by profiling sonars mounted on Autonomous Underwater Vehicles (AUV) are sparse and noisy. To solve the mapping task, AUVs need a registration algorithm to prevent maps from inconsistencies. Many scan matching algorithms are available, however, a few of them are specialized in acoustic data. In this paper, a probabilistic scan matching methodology based on Gaussian Mixtures Models (GMM) is presented and, for the first time, the Bayesian-GMM algorithm is applied in this context to model acoustic data. The scan matching problem is properly formulated using Lie groups to define pose. In addition, this methodology can return an uncertainty measure for the matching result, which is fundamental in Pose SLAM applications. This tool is implemented in a public C++library11The library repository can be found in https://bitbucket.org/gmmregistration/gmm_registration. that can process in real-time 2D and 3D scans acquired by a profiling sonar. Theoretical justification and results with real data are provided to benchmark our method against the state-of-the-art Normal Distributions Transforms (NDT) technique.