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Abstract:

An autonomous mobile robot must face the correspondence
or data association problem in order to carry out
tasks like place recognition or unknown environment mapping. In
order to put into correspondence two maps, most correspondence
methods first extract early features from robot sensor data,
then matches between features are searched and finally the
transformation that relates the maps is estimated from such
matches. However, finding explicit matches between features is a
challenging and computationally expensive task. In this paper, we
propose a new method to align obstacle maps without searching
explicit matches between features. The maps are obtained from a
stereo pair. Then, we use a vocabulary tree approach to identify
putative corresponding maps followed by a Newton minimization
algorithm to find the transformation that relates both maps. The
proposed method is evaluated on a typical office dataset showing
good performance.

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