Quite a number of approaches for solving the
simultaneous localization and mapping (SLAM) problem
exist by now. Some of them have recently been
extended to mapping environments with six degrees of
freedom (DoF) poses, yielding 6D SLAM approaches. To
demonstrate the capabilities of the respective
algorithms, it is common practice to present
generated maps and successful loop closings in large
outdoor environments. Unfortunately, it is
non-trivial to compare different 6D SLAM approaches
objectively, because ground truth data about the
outdoor environments used for demonstration is
typically unavailable. We present a novel
benchmarking method for generating this ground truth
data based on reference maps. The method is then
demonstrated by comparing the absolute performance
of some previously existing 6D SLAM algorithms which
build a large urban outdoor map.
%0 Journal Article
%1 JFR2007
%A Wulf, O.
%A Nüchter, A.
%A Hertzberg, J.
%A Wagner, B.
%D 2008
%J Journal of Field Robotics (JFR)
%K imported
%N 3
%P 148--163
%R 10.1002/rob.20234
%T Benchmarking Urban 6D SLAM
%U https://robotik.informatik.uni-wuerzburg.de/telematics/download/jfr2008.pdf
%V 25
%X Quite a number of approaches for solving the
simultaneous localization and mapping (SLAM) problem
exist by now. Some of them have recently been
extended to mapping environments with six degrees of
freedom (DoF) poses, yielding 6D SLAM approaches. To
demonstrate the capabilities of the respective
algorithms, it is common practice to present
generated maps and successful loop closings in large
outdoor environments. Unfortunately, it is
non-trivial to compare different 6D SLAM approaches
objectively, because ground truth data about the
outdoor environments used for demonstration is
typically unavailable. We present a novel
benchmarking method for generating this ground truth
data based on reference maps. The method is then
demonstrated by comparing the absolute performance
of some previously existing 6D SLAM algorithms which
build a large urban outdoor map.
@article{JFR2007,
abstract = {Quite a number of approaches for solving the
simultaneous localization and mapping (SLAM) problem
exist by now. Some of them have recently been
extended to mapping environments with six degrees of
freedom (DoF) poses, yielding 6D SLAM approaches. To
demonstrate the capabilities of the respective
algorithms, it is common practice to present
generated maps and successful loop closings in large
outdoor environments. Unfortunately, it is
non-trivial to compare different 6D SLAM approaches
objectively, because ground truth data about the
outdoor environments used for demonstration is
typically unavailable. We present a novel
benchmarking method for generating this ground truth
data based on reference maps. The method is then
demonstrated by comparing the absolute performance
of some previously existing 6D SLAM algorithms which
build a large urban outdoor map. },
added-at = {2017-09-19T13:40:53.000+0200},
author = {Wulf, O. and N{\"u}chter, A. and Hertzberg, J. and Wagner, B.},
biburl = {https://www.bibsonomy.org/bibtex/2cc56dbd786bb6f1583dec8b487dcb21d/nuechter76},
doi = {10.1002/rob.20234},
interhash = {6fbeb0645d48c1b1501619f7a045d1fd},
intrahash = {cc56dbd786bb6f1583dec8b487dcb21d},
journal = {Journal of Field Robotics (JFR)},
keywords = {imported},
month = {March},
number = 3,
pages = {148--163},
timestamp = {2017-09-29T16:01:21.000+0200},
title = {{Benchmarking Urban 6D SLAM}},
url = {https://robotik.informatik.uni-wuerzburg.de/telematics/download/jfr2008.pdf},
volume = 25,
year = 2008
}