This paper introduces a multi-level classification framework for the
semantic annotation of urban maps as provided by a mobile robot.
Environmental cues are considered for classification at different
scales. The first stage considers local scene properties using a
probabilistic bag-of-words classifier. The second stage incorporates
contextual information across a given scene (spatial context) and
across several consecutive scenes (temporal context) via a Markov
Random Field (MRF). Our approach is driven by data from an onboard
camera and 3D laser scanner and uses a combination of visual and
geometric features. By framing the classification exercise probabilistically
we take advantage of an information-theoretic bail-out policy when
evaluating class-conditional likelihoods. This efficiency, combined
with low order MRFs resulting from our two-stage approach, allows
us to generate scene labels at speeds suitable for online deployment.
We demonstrate the virtue of considering such spatial and temporal
context during the classification task and analyze the performance
of our technique on data gathered over almost 17 km of track through
a city. 漏 2009 Springer Science+Business Media, LLC.
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