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

Conference item

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 months

Authors: Kunze, L; Karaoguz, H; Young, J; Jovan, F; Folkesson, J

• Publication status: Published
• Peer review status: Peer reviewed
• Host title: Proceedings of the AAAI Fall Symposium on Reasoning and Learning in Real-World Systems for Long-Term Autonomy (LTA)
• Pages: 47-54
• Publication date: 2018-11-01
• Acceptance date: 2018-08-10
• Event title: AAAI Fall Symposium 2018, Virginia, USA, October 18–20, 2018
• Event website: https://aaai.org/Symposia/Fall/fss18.php
• Event start date: 2018-10-18
• Event end date: 2018-10-19
• Language: English