Publications
On Measuring the Accuracy of SLAM Algorithms.
by R. Kümmerle, B. Steder, C. Dornhege, M. Ruhnke, G. Grisetti, C. Stachniss, and A. Kleiner.
Journal of Autonomous Robots, 2009. [
Details]
[
Evaluation Software and Datasets]
Abstract:
In this paper, we address the problem of creating an objective benchmark for evaluating SLAM approaches. We propose a framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory. This metric uses only relative relations between poses and does not rely on a global reference frame. This overcomes serious shortcomings of approaches using a global reference frame to compute the error. Our method furthermore allows us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot. We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the robotics community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user to easily analyze and objectively compare different SLAM approaches.
BibTeX entry:
@article{kuemmerle09auro,
author = {K\"ummerle, R. and Steder, B. and Dornhege, C. and Ruhnke, M. and Grisetti, G.
and Stachniss, C. and Kleiner, A.},
doi = {http://dx.doi.org/10.1007/s10514-009-9155-6},
title = {On Measuring the Accuracy of {SLAM} Algorithms},
journal = {Journal of Autonomous Robots},
year = {2009}
}
A Comparison of SLAM Algorithms Based on a Graph of Relations
by W. Burgard and C. Stachniss and G. Grisetti and B. Steder and R. Kümmerle and C. Dornhege and
M. Ruhnke and A. Kleiner and Juan D. Tardós.
Proc. of the {IEEE/RSJ} Int. Conf. on Intelligent Robots and Systems (IROS) 2009 [
Details]
Abstract:
In this paper, we address the problem of creating an objective benchmark for comparing SLAM approaches. We propose a framework for analyzing the results of SLAM approaches based on a metric for measuring the error of the corrected trajectory. The metric uses only relative relations between poses and does not rely on a global reference frame. The idea is related to graph-based SLAM approaches in the sense that it considers the energy needed to deform the trajectory estimated by a SLAM approach to the ground truth trajectory. Our method enables us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot. We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the SLAM community. The relations have been obtained by manually matching laser-range observations. We believe that our benchmarking framework allows the user an easy analysis and objective comparisons between different SLAM approaches.
BibTeX entry:
@inproceedings{burgard09iros,
author = {W. Burgard and C. Stachniss and G. Grisetti and B. Steder and
R. K{\"u}mmerle and C. Dornhege and M. Ruhnke and A. Kleiner and
Juan D. Tard{\'o}s},
title = {A Comparison of SLAM Algorithms Based on a Graph of Relations},
booktitle = {Proc. of the {IEEE/RSJ} Int. Conf. on Intelligent Robots and Systems (IROS)},
address = {St. Louis, MO, USA},
month = oct,
year = {2009},
note = {(To appear)}
}
Unsupervised Learning of 3D Object Models from Partial Views
by M. Ruhnke, B. Steder, G. Grisetti, W. Burgard.
In Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), Kobe, Japan, 2009. [
Details]
Abstract:
In this paper we present an algorithm for learning 3D object
models from partial object observations. The input to our
algorithm is a sequence of 3D laser range scans. Models learned
from the objects are represented as point clouds. Our approach
can deal with partial views and it can robustly learn accurate
models from complex scenes. It is based on an iterative match-
ing procedure which attempts to recursively merge similar
models. The alignment between models is determined using a
novel scan registration procedure based on range images. The
decision about which models to merge is performed by spectral
clustering of a similarity matrix whose entries represent the
consistency between different models.
BibTeX entry:
@inproceedings{ruhnke09icra,
author = {Ruhnke, M. and Steder, B. and Grisetti, G. and Burgard, W},
title = {Unsupervised Learning of 3D Object Models from Partial
Views},
year = {2009},
address = {Kobe, Japan},
booktitle= {Proc.~of the IEEE Int.~Conf.~on Robotics \& Automation
(ICRA)}
}
Unüberwachtes Lernen von 3D Modellen für nicht stationäre Objekte auf volumetrischen Daten
by M. Ruhnke
Master Thesis, Albert-Ludwigs-Universität, Institut für Informatik, (Freiburg), 2008. In German. [
Details]
BibTeX entry:
@mastersthesis{ruhnke08thesis,
author = {M. Ruhnke},
title = {{U}n{\"u}berwachtes {L}ernen von 3D {M}odellen für nicht station{\"a}re {O}bjekte auf volumetrischen {D}aten},
school = {Albert-Ludwigs-Universit{\"a}t, Institut f{\"u}r Informatik},
address = {Freiburg},
year = {2008},
note = {In German},
url = {http://www.informatik.uni-freiburg.de/~ruhnke/papers/ruhnke-diplom-08.pdf}
}
RoboCupRescue - Robot League Team RescueRobots Freiburg (Germany)
by A. Kleiner, C. Dornhege, R. Kümmerle, M. Ruhnke, B. Steder, B. Nebel, P. Doherty, M. Wzorek,
P. Rudol, G. Conte, S. Durante, and D. Lundstrom.
In RoboCup 2006 (CDROM Proceedings), Team Description Paper, Rescue Robot League, (Bremen, Germany), 2006. [
Details]
BibTeX entry:
@inproceedings{kleiner06robocup,
author = {A. Kleiner and C. Dornhege and R. K{\"u}mmerle and M. Ruhnke
and B. Steder and B. Nebel and P. Doherty and M. Wzorek and P.
Rudol and G. Conte and S. Durante and D. Lundstrom},
title = {{RoboCupRescue} - Robot League Team {RescueRobots Freiburg}
(Germany)},
booktitle = {RoboCup 2006 (CDROM Proceedings), Team Description Paper,
Rescue Robot League},
address = {Bremen, Germany},
year = {2006},
url = {http://www.informatik.uni-freiburg.de/~kuemmerl/papers/kleiner-et-al-tdp2006.pdf}
}