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Utilizing Learned Motion Patterns to Predict Positions of People

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 enables a mobile robot to robustly keep track of persons in its environment and to improve its behavior. We propose a technique for learning collections of trajectories that characterize typical motion patterns of persons. Data recorded with laser-range finders is clustered using the expectation maximization algorithm. Based on the result of the clustering process we derive a Hidden Markov Model (HMM) that is applied to estimate the current and future positions of persons based on sensory input. We present several experiments carried out in different environments with a mobile robot equipped with a laser range scanner and a camera system. The results demonstrate that our approach can reliably learn motion patterns of persons, can robustly estimate and predict the positions of multiple persons, and can be used to improve the navigation behavior of a mobile robot.

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Animations:

  See mpeg-video (4.7 MB) for an experiment with a single person. The video shows a scene overview (left hand side), the results from the people tracking system which is based on laser-range data (right hand side), as well as the HMM (bottom) which is used to maintain a belief of the robot over the positions of the person. In this case we do not use vision information because we assume only one person is moving in the environment. In the HMM the red dot corresponds to the position of the person provided by the laser tracking system. The size of the squares of the states of the HMM represent the probabilty that the person is currently in the corresponding state.

  See mpeg-video (12.8 MB) for an experiment with multiple persons. The video shows the camera images (left hand side) with the areas corresponding to a person detected by the laser tracking system, as well as one HMM (right hand side). The HMM shows the belief of the robot over the position of the person which enters the corridor as second (black trousers, blue shirt).

  See animated gif (5.9 MB) for an experiment with two persons. Whereas the upper image depicts the belief about the position of person 1 the lower image shows the belief about the position of person 2. The circles are detected features. The grey value of each circle represents the similarity to the person corresponding to the HMM (the darker the more likely). In the beginning the robot was quite certain that persons 1 and 2 were in the room containing resting place 3.

  See animated gif (3.4 MB) for an experiment with a moving robot. Here the robot traveled along the corridor and looked into one of the offices where it detected person A. Whereas the robot was initially rather uncertain as to where person A was, the probability of resting place 3 seriously increased after the detection.
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