Mapping and localization of mobile robots in an
unknown environment are essential for most
high-level operations like autonomous navigation or
exploration. This paper presents a novel approach
for combining estimated trajectories, namely
curvefusion. The robot used in the experiments is
equipped with a horizontally mounted 2D profiler, a
constantly spinning 3D laser scanner and a GPS
module. The proposed algorithm first combines
trajectories from different sensors to optimize
poses of the planar three degrees of freedom (DoF)
trajectory, which is then fed into continuous-time
simultaneous localization and mapping (SLAM) to
further improve the trajectory. While
state-of-the-art multi-sensor fusion methods mainly
focus on probabilistic methods, our approach instead
adopts a deformation-based method to optimize
poses. To this end, a similarity metric for curved
shapes is introduced into the robotics community to
fuse the estimated trajectories. Additionally, a
shape-based point correspondence estimation method
is applied to the multi-sensor time
calibration. Experiments show that the proposed
fusion method can achieve relatively better
accuracy, even if the error of the trajectory before
fusion is large, which demonstrates that our method
can still maintain a certain degree of accuracy in
an environment where typical pose estimation methods
have poor performance. In addition, the proposed
time-calibration method also achieves high accuracy
in estimating point correspondences.
%0 Journal Article
%1 MDPI2020
%A Du, S.
%A Lauterbach, H. A.
%A Li, X.
%A Demisse, G. G.
%A Borrmann, D.
%A Nüchter, A.
%D 2020
%J Sensors
%K imported myown
%N 23
%P 6918
%R https://doi.org/10.3390/s20236918
%T Curvefusion --- A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration
%U https://robotik.informatik.uni-wuerzburg.de/telematics/download/sensors2020.pdf
%V 20
%X Mapping and localization of mobile robots in an
unknown environment are essential for most
high-level operations like autonomous navigation or
exploration. This paper presents a novel approach
for combining estimated trajectories, namely
curvefusion. The robot used in the experiments is
equipped with a horizontally mounted 2D profiler, a
constantly spinning 3D laser scanner and a GPS
module. The proposed algorithm first combines
trajectories from different sensors to optimize
poses of the planar three degrees of freedom (DoF)
trajectory, which is then fed into continuous-time
simultaneous localization and mapping (SLAM) to
further improve the trajectory. While
state-of-the-art multi-sensor fusion methods mainly
focus on probabilistic methods, our approach instead
adopts a deformation-based method to optimize
poses. To this end, a similarity metric for curved
shapes is introduced into the robotics community to
fuse the estimated trajectories. Additionally, a
shape-based point correspondence estimation method
is applied to the multi-sensor time
calibration. Experiments show that the proposed
fusion method can achieve relatively better
accuracy, even if the error of the trajectory before
fusion is large, which demonstrates that our method
can still maintain a certain degree of accuracy in
an environment where typical pose estimation methods
have poor performance. In addition, the proposed
time-calibration method also achieves high accuracy
in estimating point correspondences.
@article{MDPI2020,
abstract = {Mapping and localization of mobile robots in an
unknown environment are essential for most
high-level operations like autonomous navigation or
exploration. This paper presents a novel approach
for combining estimated trajectories, namely
curvefusion. The robot used in the experiments is
equipped with a horizontally mounted 2D profiler, a
constantly spinning 3D laser scanner and a GPS
module. The proposed algorithm first combines
trajectories from different sensors to optimize
poses of the planar three degrees of freedom (DoF)
trajectory, which is then fed into continuous-time
simultaneous localization and mapping (SLAM) to
further improve the trajectory. While
state-of-the-art multi-sensor fusion methods mainly
focus on probabilistic methods, our approach instead
adopts a deformation-based method to optimize
poses. To this end, a similarity metric for curved
shapes is introduced into the robotics community to
fuse the estimated trajectories. Additionally, a
shape-based point correspondence estimation method
is applied to the multi-sensor time
calibration. Experiments show that the proposed
fusion method can achieve relatively better
accuracy, even if the error of the trajectory before
fusion is large, which demonstrates that our method
can still maintain a certain degree of accuracy in
an environment where typical pose estimation methods
have poor performance. In addition, the proposed
time-calibration method also achieves high accuracy
in estimating point correspondences.},
added-at = {2020-12-03T15:22:23.000+0100},
author = {Du, S. and Lauterbach, H. A. and Li, X. and Demisse, G. G. and Borrmann, D. and N{\"u}chter, A.},
biburl = {https://www.bibsonomy.org/bibtex/231172aa3d91ad842b6f27c42b22e2f50/nuechter76},
doi = {https://doi.org/10.3390/s20236918},
interhash = {aa3bc15dc3077ed8e0f78f70d363a1af},
intrahash = {31172aa3d91ad842b6f27c42b22e2f50},
journal = {Sensors},
keywords = {imported myown},
month = {December},
number = 23,
pages = 6918,
timestamp = {2021-06-08T10:45:15.000+0200},
title = {Curvefusion --- A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration},
url = {https://robotik.informatik.uni-wuerzburg.de/telematics/download/sensors2020.pdf},
volume = 20,
year = 2020
}