Building Human Motion Map with human states estimation in indoor dynamic environments

In recent years, robots working in human living space with human-robot interaction are actively researched. To these robots, it is important to perform environmental cognition including to estimate human states around the robots and to have environment map for autonomous motion of the robots. In this research, we focus on building maps of dynamic environment. In this paper, we proposed Human Motion Map, a high-dimensional map, which incorporating human states information into results of conventional SLAM. The Human Motion Map is with high potential on further human estimation and motion planning for robots by considering the uncertainties of environment and human states.