Incremental learning of statistical motion patterns with growing hidden Markov models
IEEE Transactions on Intelligent Transportation Systems
Planning-based prediction for pedestrians
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Adaptive human motion analysis and prediction
Pattern Recognition
Who are you with and where are you going?
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Abstracting People's Trajectories for Social Robots to Proactively Approach Customers
IEEE Transactions on Robotics
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Human-aware robot navigation: A survey
Robotics and Autonomous Systems
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For many tasks robots need to operate in human populated environments. Human motion prediction is gaining importance since this helps minimizing the hinder robots cause during the execution of these tasks. The concept of social forces defines virtual repelling and attracting forces from and to obstacles and points of interest. These social forces can be used to model typical human movements given an environment and a person's intention. This work shows how such models can exploit typical motion patterns summarized by growing hidden Markov models (GHMMs) that can be learned from data online and without human intervention. An extensive series of experiments shows that exploiting a person's intended position estimated using a GHMM within a social forces based motion model yields a significant performance gain in comparison with the standard constant velocity-based models.