Robust Localization of Persons Based on Learned Motion Patterns

Whenever people move through their environments they do not move randomly. Instead, they usually follow specific trajectories or motion patterns corresponding to their intentions. Knowledge about such patterns may enable a mobile robot to robustly keep track of persons in its environment. This paper proposes a technique to derive a Hidden Markov Model (HMM) from learned motion patterns of people. This HMM is able to estimate the current and future positions of persons given knowledge about their intentions. Experimental results obtained with a mobile robot using laser and vision data collected in a typical office building with several persons illustrate the reliability and robustness of the approach. We also demonstrate that our model provides better estimates than an HMM directly learned from the data.