K. Lai and D. Fox.
3D Laser Scan Classification
Using Web Data and Domain Adaptation
Proc. of Robotics: Science and Systems, 2009
Abstract
Over the last years, object recognition has become
a more and more active field of research in robotics. An
important problem in object recognition is the need for sufficient
labeled training data to learn good classifiers. In this paper
we show how to significantly reduce the need for manually
labeled training data by leveraging data sets available on the
World Wide Web. Specifically, we show how to use objects from
Google’s 3D Warehouse to train classifiers for 3D laser scans
collected by a robot navigating through urban environments.
In order to deal with the different characteristics of the web
data and the real robot data, we additionally use a small set
of labeled 3D laser scans and perform domain adaptation. Our
experiments demonstrate that additional data taken from the 3D
Warehouse along with our domain adaptation greatly improves
the classification accuracy on real laser scans.
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