Tag Archives: Bayesian information criterion

Expectation Maximization algorithm to obtain Gaussian mixture models for ROS

I found a really good code at GitHub for fitting a Gaussian Mixture Model (GMM) with Expectation Maximization (EM) for ROS. There are so many parameters that you can change. Some of the most important ones are:

To find the optimal number of components, it uses Bayesian information criterion (BIC). There are other methods to find the optimal number of components: Minimum description length (MDL),  Akaike information criterion (AIC),  Minimum message length (MML).

Here is my code for generating a 2 Gaussian and sending them to this node:


and you need to put them in to send them to the node:


and the results are what we expect:

It also makes it possible to visualize the data in RVIZ, but first, you have to publish your tf data and set the frame name and topic names correctly in gmm_rviz_converter.h

and add a MarkerArray in RVIZ and set the topic “gmm_rviz_converter_output


References: [1], [2]