Studying table-top manipulation tasks: a robust framework for object tracking in collaboration

Lightbody, Peter, Baxter, Paul and Hanheide, Marc (2018) Studying table-top manipulation tasks: a robust framework for object tracking in collaboration. In: The 13th Annual ACM/IEEE International Conference on Human Robot Interaction, 5 - 8 March 2018, Chicago, IL, USA.

HRI_lbr_18.pdf - Whole Document

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


Table-top object manipulation is a well-established test bed on which to study both basic foundations of general human-robot interaction and more specific collaborative tasks. A prerequisite, both for studies and for actual collaborative or assistive tasks, is the robust perception of any objects involved. This paper presents a real-time capable and ROS-integrated approach, bringing together state-of-the-art detection and tracking algorithms, integrating perceptual cues from multiple cameras and solving detection, sensor fusion and tracking in one framework. The highly scalable framework was tested in a HRI use-case scenario with 25 objects being reliably tracked under significant temporary occlusions. The use-case demonstrates the suitability of the approach when working with multiple objects in small table-top environments and highlights the versatility and range of analysis available with this framework.

Keywords:Fiducial Markers, Visual Tracking, Human Robot Collaboration, bmjdoi
Subjects:G Mathematical and Computer Sciences > G440 Human-computer Interaction
G Mathematical and Computer Sciences > G740 Computer Vision
G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science
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ID Code:31204
Deposited On:07 Mar 2018 13:40

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