Dr Nick Hawes

Reader in Autonomous Intelligent Robotics

School of Computer Science
University of Birmingham
Edgbaston, Birmingham, B15 2TT
United Kingdom

Email: n.a.hawes@cs.bham.ac.uk
Twitter: @hawesie
Phone: +44 (0) 121 41 43739
Office: 133 (first floor, back right)
Office Hours: Mon 12:00, Tues 11:00 (term-time only)
Availability: Doodle MeetMe
[kunze14bootstrapping] Lars Kunze, Chris Burbridge and Nick Hawes. Bootstrapping Probabilistic Models of Qualitative Spatial Relationsfor Active Visual Object Search. In AAAI Spring Symposium 2014 on Qualitative Representations for Robots. March 2014. [pdf] [bib]
Abstract. In many real world applications, autonomous mobile robots are required to observe or retrieve objects in their environment, despite not having accurate estimates of the objects' locations. Finding objects in real-world settings is a non-trivial task, given the complexity and the dynamics of human environments. However, by understanding and exploiting the structure of such environments, e.g. where objects are commonly placed as part of everyday activities, robots can perform search tasks more efficiently and effectively than without such knowledge. In this paper we investigate how probabilistic models of qualitative spatial relations can improve the performance in object search tasks. Specifically, we learn Gaussian Mixture Models of spatial relations between object classes from descriptive statistics of real office environments. Experimental results with a range of sensor models suggest that our model improves overall performance in object search tasks.
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