gaussian_mixture_model | ROS node implementation of the Expectation Maximization

gaussian_mixture_model is a C++ library. gaussian_mixture_model has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

author: riccardo monica rimlab, department of information engineering, university of parma, italy -- introduction -- this ros (robot operating system) package was developed to fit a gaussian mixture model (gmm) to a trajectory, through expectation maximization (em) algorithm. the main node, gaussian_mixture_model_node, can also be used alone to fit a gmm to arbitrary data,. two additional nodes are provided: gaussian_mixture_model_rviz_converter, for visualization, and gaussian_mixture_model_trajectory_converter, for conversion of a trajectory into the required format. -- gaussian_mixture_model_node -- this node fits the gmm on arbitrary data. the node operates on a set of samples, i.e. tuples of real numbers. these tuples are organized as a matrix, one sample for each row. the em algorithm treats the samples as an unordered set. the input of the node is a std_msgs/float32multiarray message. this message must have the layout of a 2d matrix. the first dimension of the layout (at position 0) has size equal to the number of samples (number of rows). the second dimension of the layout has size equal to the number of elements in each tuple (number of columns). the data field of the message contains the matrix, in row-major order. the input messages are queued and processed in order by the