SOMA: a framework for understanding change in everyday environments using Semantic Object Maps

Understanding change related to the dynamics of people and objects in everyday environments is a challenging problem. At the same time, it is a key requirement in many applications of autonomous mobile service robots. In this paper we present a novel semantic mapping framework which maps locations of objects, regions of interest, and movements of people over time. Our aim with this framework is twofold: (1) we want to allow robots to reason semantically, spatially, and temporally about their environment, and (2) we want to enable researchers to investigate research questions in the context of long-term scenarios in dynamic environments. Experimental results demonstrate the effectiveness of the framework which was deployed on mobile robot systems in real-world environments over several month