Spherical robots are a format that has not been
thoroughly explored for the application of mobile
mapping. In contrast to other designs, it provides
some unique advantages. Among those is a spherical
shell that protects internal sensors and actuators
from possible harsh environments, as well as an
inherent rotation for locomotion that enables
measurements in all directions. Mobile mapping
always requires a high-precise pose knowledge to
obtain consistent and correct environment maps. This
is typically done by a combination of external
reference sensors such as Global Navigation
Satellite System (GNSS) measurements and inertial
measurements or by coarsely estimating the pose
using inertial measurement units (IMUs) and post
processing the data by registering the different
measurements to each other. In indoor environments,
the GNSS reference is not an option. Hence many
mobile mapping applications turn to the second
option. An advantage of indoor environments is that
human-made environments usually have a certain
structure, such as parallel and perpendicular
planes. We propose a registration procedure that
exploits this structure by minimizing the distance
of measured points to a corresponding
plane. Further, we evaluate the procedure on a
simulated dataset of an ideal corridor and on an
experimentally acquired dataset with different
motion profiles. We show that we nearly reproduce
the ground truth for the simulated dataset and
improve the average point-to-point distance to a
reference scan in the experimental dataset. The
presented algorithms are required to work completely
autonomously.
%0 Journal Article
%1 JPRS_2018
%A Arzberger, F.
%A Bredenbeck, A.
%A Zevering, J.
%A Borrmann, D.
%A Nüchter, A.
%D 2021
%J ISPRS Open Journal of Photogrammetry and Remote Sensing (OJPRS)
%K imported myown
%P 100004
%R 10.1016/j.ophoto.2021.100004
%T Towards spherical robots for mobile mapping in human made environments
%U https://robotik.informatik.uni-wuerzburg.de/telematics/download/ojprs2021.pdf
%V 1
%X Spherical robots are a format that has not been
thoroughly explored for the application of mobile
mapping. In contrast to other designs, it provides
some unique advantages. Among those is a spherical
shell that protects internal sensors and actuators
from possible harsh environments, as well as an
inherent rotation for locomotion that enables
measurements in all directions. Mobile mapping
always requires a high-precise pose knowledge to
obtain consistent and correct environment maps. This
is typically done by a combination of external
reference sensors such as Global Navigation
Satellite System (GNSS) measurements and inertial
measurements or by coarsely estimating the pose
using inertial measurement units (IMUs) and post
processing the data by registering the different
measurements to each other. In indoor environments,
the GNSS reference is not an option. Hence many
mobile mapping applications turn to the second
option. An advantage of indoor environments is that
human-made environments usually have a certain
structure, such as parallel and perpendicular
planes. We propose a registration procedure that
exploits this structure by minimizing the distance
of measured points to a corresponding
plane. Further, we evaluate the procedure on a
simulated dataset of an ideal corridor and on an
experimentally acquired dataset with different
motion profiles. We show that we nearly reproduce
the ground truth for the simulated dataset and
improve the average point-to-point distance to a
reference scan in the experimental dataset. The
presented algorithms are required to work completely
autonomously.
@article{JPRS_2018,
abstract = {Spherical robots are a format that has not been
thoroughly explored for the application of mobile
mapping. In contrast to other designs, it provides
some unique advantages. Among those is a spherical
shell that protects internal sensors and actuators
from possible harsh environments, as well as an
inherent rotation for locomotion that enables
measurements in all directions. Mobile mapping
always requires a high-precise pose knowledge to
obtain consistent and correct environment maps. This
is typically done by a combination of external
reference sensors such as Global Navigation
Satellite System (GNSS) measurements and inertial
measurements or by coarsely estimating the pose
using inertial measurement units (IMUs) and post
processing the data by registering the different
measurements to each other. In indoor environments,
the GNSS reference is not an option. Hence many
mobile mapping applications turn to the second
option. An advantage of indoor environments is that
human-made environments usually have a certain
structure, such as parallel and perpendicular
planes. We propose a registration procedure that
exploits this structure by minimizing the distance
of measured points to a corresponding
plane. Further, we evaluate the procedure on a
simulated dataset of an ideal corridor and on an
experimentally acquired dataset with different
motion profiles. We show that we nearly reproduce
the ground truth for the simulated dataset and
improve the average point-to-point distance to a
reference scan in the experimental dataset. The
presented algorithms are required to work completely
autonomously.
},
added-at = {2021-09-16T21:29:48.000+0200},
author = {Arzberger, F. and Bredenbeck, A. and Zevering, J. and Borrmann, D. and N{\"u}chter, A.},
biburl = {https://www.bibsonomy.org/bibtex/26127b6697992a7f0cd50ac94e329c55f/nuechter76},
doi = {10.1016/j.ophoto.2021.100004},
interhash = {80e5799d6b6434d99a9bc97b1c31a170},
intrahash = {6127b6697992a7f0cd50ac94e329c55f},
issn = {2667-3932},
journal = {ISPRS Open Journal of Photogrammetry and Remote Sensing (OJPRS)},
keywords = {imported myown},
pages = 100004,
timestamp = {2024-07-30T17:32:29.000+0200},
title = {Towards spherical robots for mobile mapping in human made environments},
url = {https://robotik.informatik.uni-wuerzburg.de/telematics/download/ojprs2021.pdf},
volume = 1,
year = 2021
}