Generating human motion transition map in indoor environment and analyzing human behavior by geographical clustering

In recent years, robots working in human living space with human-robot interactions are actively studied. To these robots, it is important to perform environmental cognition not only building environment map for autonomous motion of the robots but also estimating presences of human around the robots. In this study, by utilizing human state estimation function and SLAM based mapping technology, a concept and architecture of Human Motion Map by representing human behavior in the human living space as a hybrid map system are proposed. Beyond the conventional map which represents the existence of wall and objects, Human Motion Map represents not only the existence of humans in a particular location but also motion distributions. With recent improvements of the cloud computing technology, Human Motion Map can be accumulated as a kind of big data while measurements of robots are performed continuingly while it is moving around. In this paper, we propose a motion feature classification algorithm for clustering human motions geographically. Some experiment result of basic motion feature extraction, geographical clustering, and human motion behavior analyzing are provided for illustrating the validity of proposed algorithm.