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.
Learning Motion Patterns of People for Compliant Robot Motion
M. Bennewitz, W. Burgard, G. Cielniak, and S. Thrun. In: The International Journal of Robotics Research, Vol. 24, Number 1, 2005.
Mobile Robot Navigation
in Dynamic Environments (PhD Thesis)
M. Bennewitz, University of Freiburg, 2004. Webpage with detailed information.
- Where is ...?
Learning and Utilizing Motion Patterns of Persons with Mobile Robots.
G. Cielniak, M. Bennewitz, and W. Burgard. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2003.
Localization of Persons Based on Learned Motion
G. Cielniak, M. Bennewitz, and W. Burgard. In: Proceedings of the European Conference on Mobile Robots (ECMR), 2003.
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.
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
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.|