Detecting, tracking and predicting human motion inside an industrial robotic cell using a map-based particle filtering strategy

In order to enable safe and efficient human-robot interaction it is beneficial for the robot control system to be able not only to detect the presence and track the motion of human workers entering the robotic cell, but also to predict in the least possible time their trajectory and the area they are heading to. This paper proposes an innovative particle filtering strategy addressing at the same time the problems of Human Detection and Tracking and Intention Estimation, based on low-cost commercial RGB surveillance cameras, a map of the robotic cell environment, and a probabilistic description of the trajectories followed by human workers inside the cell. Results of several validation experiments are presented.