Journal ArticleDOI
Learning Motion Patterns of People for Compliant Robot Motion
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TLDR
A technique for learning collections of trajectories that characterize typical motion patterns of persons and how to incorporate the probabilistic belief about the potential trajectories of persons into the path planning process of a mobile robot is proposed.Abstract:
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. In this paper we propose a technique for learning collections of trajectories that characterize typical motion patterns of persons. Data recorded with laser-range finders are clustered using the expectation maximization algorithm. Based on the result of the clustering process, we derive a hidden Markov model that is applied to estimate the current and future positions of persons based on sensory input. We also describe how to incorporate the probabilistic belief about the potential trajectories of persons into the path planning process of a mobile robot. 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 positions of persons, and can be used to improve the navigation behavior of a mobile robot.read more
Citations
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Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI
Human-aware robot navigation: A survey
TL;DR: This paper provides a survey of existing approaches to human-aware navigation and offers a general classification scheme for the presented methods.
Proceedings ArticleDOI
Unfreezing the robot: Navigation in dense, interacting crowds
Pete Trautman,Andreas Krause +1 more
TL;DR: IGP is developed, a nonparametric statistical model based on dependent output Gaussian processes that can estimate crowd interaction from data that naturally captures the non-Markov nature of agent trajectories, as well as their goal-driven navigation.
Journal ArticleDOI
Human motion trajectory prediction: a survey:
Andrey Rudenko,Andrey Rudenko,Luigi Palmieri,Michael Herman,Kris M. Kitani,Dariu M. Gavrila,Kai O. Arras +6 more
TL;DR: In this article, the ability of intelligent autonomous systems to perceive, understand, and anticipate human behavior becomes increasingly important in a growing number of intelligent systems in human environments, and the ability to do so is discussed.
Journal ArticleDOI
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
Lin Liao,Dieter Fox,Henry Kautz +2 more
TL;DR: The proposed system is able to robustly estimate a person’s activities using a model that is trained from data collected by other persons, and shows significant improvements over existing techniques.
References
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TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
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Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.