Human-centric partitioning of the environment

In this paper, we present an object based approach for human-centric partitioning of the environment. Our approach for determining the human-centric regions is to detect the objects that are commonly associated with frequent human presence. In order to detect these objects, we employ state of the art perception techniques. The detected objects are stored with their spatio-temporal information in the robot's memory to be later used for generating the regions. The advantages of our method is that it is autonomous, requires only a small set of perceptual data and does not even require people to be present while generating the regions. The generated regions are validated using a 1-month dataset collected in an indoor office environment. The experimental results show that although a small set of perceptual data is used, the regions are generated at densely occupied locations.