Autonomous Identification of Human Activity Regions

by Lin Qi

Institution: KTH
Year: 2017
Keywords: Human Activity Region; Robot; Human Tracking; Computer Sciences; Datavetenskap (datalogi)
Posted: 02/01/2018
Record ID: 2195853
Full text PDF: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-212052


Human activity regions (HARs) are human-centric semantic partitions where observing and/or interacting with humans is likely in indoor environments. HARs are useful for achieving successful human-robot interaction, such as in safe navigation around a building or to know where to be able to assist humans in their activities. In this thesis, a system is designed for generating HARs automatically based on data recorded by robots. This approach to generating HARs is to cluster the areas that are commonly associated with frequent human presence. In order to detect human positions, we employ state-of-the-art perception techniques. The environment that the robot patrols is assumed to be an indoor environment such as an office. We show how we can generate HARs in correct regions by clustering human position data. The experimental evaluations show that we can do so in different indoor environments, with data acquired from different sensors and that the system can handle noise. Mnskliga aktivitetsregioner, HARs (Human Activity Regions) r mnniskocentreraderegioner som ger en semantisk partitionering av inomhusmiljer. HARs r anvndbara fr att uppn vl fungerande mnniskarobot- interaktioner. I denna avhandling utformas ett system fr att generera HARs automatiskt baserat p data frn robotar. Detta grs genom att klustra observationer av mnniskor fr att p s vis f fram de omrden som r associerade med frekvent mnsklig nrvaro. Experiment visar att systemet kan hantera data som registrerats av olika sensorer i olika inomhusmiljer och att det r robust. Framfrallt genererar systemet en plitlig partitionering av miljn.