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THÖR: Human-Robot Navigation Data Collection and Accurate Motion Trajectories Dataset
Örebro University, School of Science and Technology. Robotics Research, Bosch Corporate Research, Stuttgart, Germany. (MRO Lab, AASS)ORCID iD: 0000-0002-8380-4113
Örebro University, School of Science and Technology. (MRO Lab, AASS)ORCID iD: 0000-0002-9503-0602
Örebro University, School of Science and Technology. (MRO Lab, AASS)ORCID iD: 0000-0002-9545-9871
Örebro University, School of Science and Technology. (MRO Lab, AASS)ORCID iD: 0000-0002-8380-4113
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2020 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 5, no 2, p. 676-682Article in journal (Refereed) Published
Abstract [en]

Understanding human behavior is key for robots and intelligent systems that share a space with people. Accordingly, research that enables such systems to perceive, track, learn and predict human behavior as well as to plan and interact with humans has received increasing attention over the last years. The availability of large human motion datasets that contain relevant levels of difficulty is fundamental to this research. Existing datasets are often limited in terms of information content, annotation quality or variability of human behavior. In this paper, we present THÖR, a new dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for position, head orientation, gaze direction, social grouping, obstacles map and goal coordinates. THÖR also contains sensor data collected by a 3D lidar and involves a mobile robot navigating the space. We propose a set of metrics to quantitatively analyze motion trajectory datasets such as the average tracking duration, ground truth noise, curvature and speed variation of the trajectories. In comparison to prior art, our dataset has a larger variety in human motion behavior, is less noisy, and contains annotations at higher frequencies.

Place, publisher, year, edition, pages
IEEE, 2020. Vol. 5, no 2, p. 676-682
Keywords [en]
Social Human-Robot Interaction, Motion and Path Planning, Human Detection and Tracking
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-79266DOI: 10.1109/LRA.2020.2965416Scopus ID: 2-s2.0-85078707058OAI: oai:DiVA.org:oru-79266DiVA, id: diva2:1387088
Funder
EU, Horizon 2020, 732737 (ILIAD)Knowledge Foundation, 20140220 (AIR)Available from: 2020-01-20 Created: 2020-01-20 Last updated: 2020-03-17Bibliographically approved

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Rudenko, AndreyKucner, Tomasz PiotrSwaminathan, Chittaranjan SrinivasLilienthal, Achim J.

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Rudenko, AndreyKucner, Tomasz PiotrSwaminathan, Chittaranjan SrinivasChadalavada, Ravi TejaLilienthal, Achim J.
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