Knowing and predicting the locations of people moving through an environment is a key component of many proactive service applications, including mobile robots. Depending on the task and the available sensors, we apply joint probabilistic data association filters, Rao-Blackwellised particle filters, and Voronoi-based particle filters to estimate locations of people. Such estimates build the foundations for learning typical motion patterns of people, as used in the activity recognition project.
Various animations related to people tracking can be found on the particle filters project page.
Example: WiFi-based people tracking using MCL and Gaussian Process sensor models.
This animation shows tracking of a person carrying a laptop measuring wireless signal strengths. The approach uses MCL to track a person's location on a graph structure, and Gaussian processes to model the signal strengths of access points.
