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@INPROCEEDINGS{Martinez-Gomez_ICRA2011_2011,
         author = {Martinez-Gomez, Jesus and Caputo, Barbara},
       projects = {Idiap, SNSF-MULTI},
          title = {Towards semi-supervised learning of semantic spatial concepts},
      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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2011/Martinez-Gomez_ICRA2011_2011.pdf}
}