Abstract

The efficiency of autonomous robots depends on how well they understand their operating environment. While most of the traditional environment models focus on the spatial representation, long-term mobile robot operation in human populated environments requires that the robots have a basic model of human behaviour.
We present a framework that allows us to retrieve and represent aggregate human behaviour in large, populated environments on extended temporal scales. Our approach, based on time-varying Poisson process models and spectral analysis, efficiently retrieves long-term, re-occurring patterns of human activity from robot-gathered observations and uses these patterns to i) predict human activity level at particular times and places and ii) classify locations based on their periodic patterns of activity. The application of our framework on real-world data, gathered by a mobile robot operating in an indoor environment for one month, indicates that its predictive capabilities outperform other temporal modelling methods while being computationally more efficient. The experiment also demonstrates that spectral signatures act as features that allow us to classify room types which semantically match with humans' expectations.