Mapping Dynamic Environment Using Gaussian Mixture Model

In this paper, we address the problem of mapping dynamic environments with detection of moving objects. The static and moving objects are modeled as the components in a Gaussian mixture model (GMM). By recursively learning of GMM, the components corresponding to the static objects will have higher weights while the components corresponding to the moving objects will have lower weights. At each time step, a number of components with highest weights are adaptively selected as the background map and the new observations which do not match with the background map are classified as the foreground map. In order to obtain the expected observation, from which the Gaussian mixture model is learned, we use a particle filter to approximate the posterior probability density function of the pose of the robot and update it sequentially. Also an on-line algorithm is proposed and some simulations on a simple one-dimensional example indicate that our approach is feasible.