Nowadays, the advance of the technology allows robots to acquire dense point clouds decreasing the price and increasing the performance. However, it is a hard task to deal with due to the large amount of points, the redundancy and the noise. This paper proposes an adaptable system to build a 3D feature model of point clouds using Gaussian Mixture Models. These 3D models are used in order to detect changes in the autonomous robot's working environment. The presented work describes an efficient change detection system based on two consecutive stages. First, a top-down approach estimates features using Gaussian Mixture Models. The presented new approach improves the performance of previous related works in terms of computational load and robustness, nevertheless the system is selection criteria dependent. Thus, the efficiency of different selection criteria are evaluated and compared in this paper. Experimental results demonstrate that the Minimum Distance Length (MDL) criteria outperforms the other studied methods. In the second stage, a change detection method is performed using the previously estimate Mixture of Gaussians. The proposed full system is able to detect changes using Gaussian Mixture Models with a reduced computational cost in relation to state-of-art algorithms.