Slawomir Sander (Grzonka)

Giorgio Grisetti, Slawomir Grzonka, Cyrill Stachniss, Patrick Pfaff and Wolfram Burgard

Efficient Estimation of Accurate Maximum Likelihood Maps in 3D

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.


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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