Place categorization and semantic mapping on a mobile robot

Sunderhauf, Niko, Dayoub, Feras, McMahon, Sean, Talbot, Ben, Schulz, Ruth, Corke, Peter, Wyeth, Gordon, Upcroft, Ben, & Milford, Michael (2016) Place categorization and semantic mapping on a mobile robot. In Proceedings of the International Conference on Robotics and Automation, IEEE, Stockholm, Sweden. (In Press)

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In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot with-out environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot’s behaviour during navigation tasks. The system is made available to the community as a ROS module.

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2 citations in Scopus
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ID Code: 95288
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: computer vision, machine learning
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Divisions: Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2016 IEEE
Deposited On: 03 May 2016 02:03
Last Modified: 23 Aug 2016 15:56

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