Static force field representation of environments based on agents nonlinear motions
This work addresses environment modeling for moving agents, such as pedestrians, but appears incremental as it builds on existing force field and switching model concepts.
The paper tackles the problem of representing environments as static force fields based on agents' nonlinear motions, learning attractive areas from velocity fields and deriving a map of their locations, ranges, and intensities in an online manner. It was evaluated on synthetic data and applied to real pedestrian trajectories in an indoor environment.
This paper presents a methodology that aims at the incremental representation of areas inside environments in terms of attractive forces. It is proposed a parametric representation of velocity fields ruling the dynamics of moving agents. It is assumed that attractive spots in the environment are responsible for modifying the motion of agents. A switching model is used to describe near and far velocity fields, which in turn are used to learn attractive characteristics of environments. The effect of such areas is considered radial over all the scene. Based on the estimation of attractive areas, a map that describes their effects in terms of their localizations, ranges of action, and intensities is derived in an online way. Information of static attractive areas is added dynamically into a set of filters that describes possible interactions between moving agents and an environment. The proposed approach is first evaluated on synthetic data; posteriorly, the method is applied on real trajectories coming from moving pedestrians in an indoor environment.