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 [BibTeX] [Marc21]
Towards semi-supervised learning of semantic spatial concepts
Type of publication: Conference paper
Citation: Martinez-Gomez_ICRA2011_2011
Publication status: Accepted
Booktitle: IEEE International Conference on Robotics and Automation
Year: 2011
Abstract: The ability of building robust semantic space representations of environments is crucial for the development of truly autonomous robots. This task, inherently connected with cognition, is traditionally achieved by training the robot with a supervised learning phase. We argue that the design of robust and autonomous systems would greatly benefit from adopting a semi-supervised online learning approach. Indeed, the support of open-ended, lifelong learning is fundamental in order to cope with the dazzling variability of the real world, and online learning provides precisely this kind of ability. Here we focus on the robot place recognition problem, and we present an online place classification algorithm that is able to detect gap in its own knowledge based on a confidence measure. For every incoming new image frame, the method is able to decide if (a) it is a known room with a familiar appearance, (b) it is a known room with a challenging appearance, or (c) it is a new, unknown room. Experiments on a subset of the challenging COLD database show the promise of our approach.
Projects Idiap
Authors Martinez-Gomez, Jesus
Caputo, Barbara
Added by: [UNK]
Total mark: 0
  • Martinez-Gomez_ICRA2011_2011.pdf