This publication describes a 2D Simultaneous
Localization and Mapping approach applicable to
multiple mobile robots. The presented strategy uses
data of 2D LIDAR sensors to build a dynamic
representation based on Signed Distance Functions. A
multi-threaded software architecture performs
registration and data integration in parallel
allowing for drift-reduced pose estimation of
multiple robots. Experiments are provided
demonstrating the application with single and
multiple robot mapping using simulated data, public
accessible recorded data as well as two actual
robots operating in a comparably large area.
%0 Conference Paper
%1 ICARSC2015
%A Koch, P.
%A May, S.
%A Schmidpeter, M.
%A Kühn, M.
%A Pfitzner, C.
%A Merkl, C.
%A Koch, R.
%A Fees, M.
%A Martin, J.
%A Nüchter, A.
%B Proceedings of the IEEE International Conference on
Autonomous Robot Systems and Competitions (ICARSC
'15)
%C Vila Real, Portugal
%D 2015
%K imported
%P 77--82
%R 10.1109/ICARSC.2015.18
%T Multi-Robot Localization and Mapping based on
Signed Distance Functions
%U https://robotik.informatik.uni-wuerzburg.de/telematics/download/icarsc2015.pdf
%X This publication describes a 2D Simultaneous
Localization and Mapping approach applicable to
multiple mobile robots. The presented strategy uses
data of 2D LIDAR sensors to build a dynamic
representation based on Signed Distance Functions. A
multi-threaded software architecture performs
registration and data integration in parallel
allowing for drift-reduced pose estimation of
multiple robots. Experiments are provided
demonstrating the application with single and
multiple robot mapping using simulated data, public
accessible recorded data as well as two actual
robots operating in a comparably large area.
@inproceedings{ICARSC2015,
abstract = {This publication describes a 2D Simultaneous
Localization and Mapping approach applicable to
multiple mobile robots. The presented strategy uses
data of 2D LIDAR sensors to build a dynamic
representation based on Signed Distance Functions. A
multi-threaded software architecture performs
registration and data integration in parallel
allowing for drift-reduced pose estimation of
multiple robots. Experiments are provided
demonstrating the application with single and
multiple robot mapping using simulated data, public
accessible recorded data as well as two actual
robots operating in a comparably large area.},
added-at = {2017-09-19T13:40:53.000+0200},
address = {Vila Real, Portugal},
author = {Koch, P. and May, S. and Schmidpeter, M. and K{\"u}hn, M. and Pfitzner, C. and Merkl, C. and Koch, R. and Fees, M. and Martin, J. and N{\"u}chter, A.},
biburl = {https://www.bibsonomy.org/bibtex/22d8147149bbbe386b8601779cbcf3670/nuechter76},
booktitle = {Proceedings of the IEEE International Conference on
Autonomous Robot Systems and Competitions (ICARSC
'15)},
doi = {10.1109/ICARSC.2015.18},
interhash = {82b3361e60a6eb5d68f32a45bff78d19},
intrahash = {2d8147149bbbe386b8601779cbcf3670},
keywords = {imported},
month = {April},
pages = {77--82},
timestamp = {2017-09-29T16:01:21.000+0200},
title = {{Multi-Robot Localization and Mapping based on
Signed Distance Functions}},
url = {https://robotik.informatik.uni-wuerzburg.de/telematics/download/icarsc2015.pdf},
year = 2015
}