Giorgio Grisetti, Slawomir Grzonka, Cyrill Stachniss, Patrick Pfaff and Wolfram Burgard
Efficient Estimation of Accurate Maximum Likelihood Maps in 3D
Abstract:
Learning maps is one of the fundamental tasks of mobile robots. In
the past, numerous efficient approaches to map learning have been
proposed. Most of them, however, assume that the robot lives on a
plane. In this paper, we consider the problem of learning maps with
mobile robots that operate in non-flat environments and apply maximum
likelihood techniques to solve the graph-based SLAM problem. Due to
the non-commutativity of the rotational angles in~3D, major problems
arise when applying approaches designed for the two-dimensional world.
The non-commutativity introduces serious difficulties when
distributing a rotational error over a sequence of poses. In this
paper, we present an efficient solution to the SLAM problem that is
able to distribute a rotational error over a sequence of nodes. Our
approach applies a variant of gradient descent to solve the error
minimization problem. We implemented our technique and tested it on
large simulated and real world datasets. We furthermore compared our
approach to one of the so far best methods for three-dimensional pose
correction. As the experiments illustrate, our technique converges
significantly faster to an accurate map with low error and is able to
correct maps with bigger noise than existing methods.
Bibtex:
@InProceedings{grisetti07iros,
author = {Grisetti, G. and Grzonka, S. and Stachniss, C. and Pfaff, P. and Burgard, W.},
title = {Efficient Estimation of Accurate Maximum Likelihood Maps in 3D},
booktitle ={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
ADDRESS = {San Diego, CA, USA},
year = 2007,
}
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