Robot semantic mapping through human activity recognition: A wearable sensing and computing approach

Semantic information can help robots understand unknown environments better. In order to obtain semantic information efficiently and link it to a metric map, we present a new robot semantic mapping approach through human activity recognition in a human-robot coexisting environment. An intelligent mobile robot platform called ASCCbot creates a metric map while wearable motion sensors attached to the human body are used to recognize human activities. Combining pre-learned models of activity-furniture correlation and location-furniture correlation, the robot determines the probability distribution of the furniture types through a Bayesian framework and labels them on the metric map. Computer simulations and real experiments demonstrate that the proposed approach is able to create a semantic map of an indoor environment effectively.