R. Triebel and W. Burgard
Improving Simultaneous Localization and Mapping in 3D Using Global Constraints
Proc. of the National
Conference on Artificial Intelligence (AAAI)
Abstract
Recently, the problem of learning volumetric maps from
three-dimensional range data has become quite popular in the context of mobile
robotics. One of the key challenges in this context is to reduce the overall
amount of data. The smaller the number of data points, however, the fewer
information is available to register the scans and to compute a consistent
map. In this paper we present a novel approach that estimates global
constraints from the data and utilizes these constraints to improve the
registration process. In our current system we simultaneously minimize the
distance between scans and the distance of edges from planes extracted from
the edges to obtain highly accurate three-dimensional models of the
environment. Several experiments carried out in simulation as well as with
three-dimensional data obtained with a mobile robot in an outdoor environment
we show that our approach yields seriously more accurate models compared to a
standard approach that does not utilize the global constraints.
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Bibtex
@string{aaai = "Proc.~of the National Conference on Artificial Intelligence"}
@InProceedings{triebel05aaai,
TITLE = {Improving Simultaneous Localization and Mapping in 3D Using Global Constraints},
AUTHOR = {Triebel, R. and Burgard, W.},
BOOKTITLE = aaai,
YEAR = {2005},
}