M. Bennewitz, W. Burgard, and S. Thrun. Using EM to learn motion behaviors of persons with mobile robots. In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002. Home/Search Document Details and Download
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Foundations of Assisted Cognition Systems - Kautz, Etzioni, Fox, Weld (2003) (8 citations) (Correct)
....the GPS data in Figure 1 shows that the person always takes the same bike trail to the university, which suggests representing the complete path as one abstract action. Clustering continuous trajectories into discrete segments can be formulated as an incomplete data problem, as shown e.g. by [96, 12, 106] for the case of linear Gaussian models. Hence, using expectation maximization (EM) 36] we can extract discrete actions from user data. By organizing locations and objects in an abstraction hierarchy (e.g. a specific bus instance, the buses on one route, the class of all buses, means of ....
M. Bennewitz, W. Burgard, and S. Thrun. Using EM to learn motion behaviors of persons with mobile robots. In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002.
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.... estimation of the location of people in indoor environments has gained increased attention in the robotics community [7; 14; 10] This is mainly due to the fact that knowledge about the position and the motion patterns of people can help mobile robots to better interact with people, as stated by [2] . Most existing approaches to people tracking rely on laser range finders [7; 2; 14] or cameras [10] A key advantage of these sensors is their location accuracy. Unfortunately, they do not provide information about the identity of people. Recently, especially the ubiquitous computing community ....
....level such as which room or hallway she is in. Such discrete, abstract location information additionally provides an ideal representation for learning patterns in a person s long term behavior. Note that pattern discovery in continuous space trajectories often requires supervised learning methods [2] . Based on these observations, we introduce a novel approach to estimating the locations of people using sparse and noisy sensor data collected by id sensors. The key idea of our approach is to track the locations of people on Voronoi graphs [5] which allow us to naturally represent typical ....
M. Bennewitz, W. Burgard, and S. Thrun. Using EM to learn motion behaviors of persons with mobile robots. In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002.
Where is . . . ? Learning and Utilizing Motion.. - Cielniak, Bennewitz.. Self-citation (Bennewitz Burgard) (Correct)
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M. Bennewitz, W. Burgard, and S. Thrun. Using EM to learn motion behaviors of persons with mobile robots. In Proc. of the Int. Conference on Intelligent Robots and Systems (IROS), 2002.
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M. Bennewitz, W. Burgard, and S. Thrun. Using EM to learn motion behaviors of persons with mobile robots. In Proceedings of the Conference on Intelligent Robots and Systems (IROS), Lausanne, Switzerland, 2002.
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M. Bennewitz, W. Burgard, and S. Thrun. Using EM to learn motion behaviors of persons with mobile robots. In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2002.
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M. Bennewitz, W. Burgard, and S. Thrun. Using EM to learn motion behaviors of persons with mobile robots. In Proc. of the Int. Conference on Intelligent Robots and Systems (IROS), 2002.
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M. Bennewitz, W. Burgard, and S. Thrun. Using EM to learn motion behaviors of persons with mobile robots. In Proc. of the Int. Conference on Intelligent Robots and Systems (IROS), 2002.
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M. Bennewitz, W. Burgard, and S. Thrun. Using EM to learn motion behaviors of persons with mobile robots. In Proc. of the Int. Conference on Intelligent Robots and Systems (IROS), 2002.
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M. Bennewitz, W. Burgard, and S. Thrun. Using EM to learn motion behaviors of persons with mobile robots. In Proc. of the Int. Conference on Intelligent Robots and Systems (IROS), 2002.
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M. Bennewitz, W. Burgard, and S. Thrun. Using EM to learn motion behaviors of persons with mobile robots. IROS-02.
A Learning Algorithm for Localizing People Based.. - Berna, Lisien.. (2003) (2 citations) Self-citation (Thrun) (Correct)
....0 10 20 30 40 50 60 70 80 90 Distance along corridor (m) Signal strength Figure 5: The wireless signal strength for one access point over three separate traversals of a straight corridor. the lines of the literature of learning models from unlabeled data [3; 14; 16] and particularly [2] . Since we use particle filters for tracking, generating training signals for the parameters b l,j can be done elegantly by counting. In particular, assume that we are given a specific set of parameters, which define the motion model in the particular filter described in the previous section. ....
M. Bennewitz, W. Burgard, and S. Thrun. Using EM to learn motion behaviors of persons with mobile robots. In Proceedings of the Conference on Intelligent Robots and Systems (IROS), Lausanne, Switzerland, 2002.
Voronoi Tracking: Location Estimation Using Sparse and Noisy.. - Lin Liao Dieter (2003) (3 citations) (Correct)
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M. Bennewitz, W. Burgard, and S. Thrun. Using EM to learn motion behaviors of persons with mobile robots. In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002.
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M. Bennewitz, W. Burgard, and S. Thrun. Using EM to learn motion behaviors of persons with mobile robots. In Proceedings of the Conference on Intelligent Robots and Systems (IROS), Lausanne, Switzerland, 2002. Available from: http://robots.stanford.edu/papers/ bennewitz.02iros.html.
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M. Bennewitz, W. Burgard, and S. Thrun. Using EM to learn motion behaviors of persons with mobile robots. In Proceedings of the Conference on Intelligent Robots and Systems (IROS), 2002.
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M. Bennewitz, W. Burgard, and S. Thrun. Using EM to learn motion behaviors of persons with mobile robots. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots & Systems (IROS), 2002.
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M. Bennewitz, W. Burgard, and S. Thrun, "Using EM to Learn Motion Behaviors of Persons with Mobile Robots," in Int. Conf on Intelligent Robots and Systems, 2002.
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