Subterranean positioning for a semi-autonomous robot supporting emergency task forces

Eva Maria Reitbauer; Christoph Schmied; Hamid Didari Khamseh Motlagh

doi:10.1109/ICL-GNSS54081.2022.9797032

Subterranean positioning for a semi-autonomous robot supporting emergency task forces

Eva Maria Reitbauer^*, Christoph Schmied, Hamid Didari Khamseh Motlagh

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

This paper proposes a positioning algorithm for a semi-autonomous robot in subterranean scenarios. The robot is equipped with positioning sensors, imaging sensors, and sensors to detect hazardous materials. The sensors can be used to automatically generate a site map to increase safety for emergency forces. To create an accurate map, the position and attitude of the robot have to be determined. This is done using an extended Kalman filter which fuses data from LIDAR, wheel odometry, and a MEMS IMU. Tests were carried out in a tunnel in Eisenerz, Austria. To evaluate the achievable accuracy, the estimated position of the filter is compared to a ground truth. The results show that with the developed sensor fusion algorithm, a horizontal positioning error of 1.07% of the traveled distance can be achieved.

Original language	English
Title of host publication	2022 International Conference on Localization and GNSS (ICL-GNSS)
Editors	Jari Nurmi, Elena-Simona Lohan, Joaquin Torres Sospedra, Heidi Kuusniemi, Aleksandr Ometov
Publisher	IEEE Xplore
Number of pages	7
ISBN (Electronic)	9781665405751
DOIs	https://doi.org/10.1109/ICL-GNSS54081.2022.9797032
Publication status	Published - Jun 2022
Event	2022 International Conference on Localization and GNSS: ICL-GNSS 2022 - Tampere, Finland Duration: 7 Jun 2022 → 9 Jun 2022

Conference

Conference	2022 International Conference on Localization and GNSS
Abbreviated title	ICL-GNSS 2022
Country/Territory	Finland
City	Tampere
Period	7/06/22 → 9/06/22

Keywords

extended Kalman filtering
GICP
IMU
LIDAR
odometry
semi-autonomous robot
subterranean positioning

ASJC Scopus subject areas

Aerospace Engineering
Computer Networks and Communications

Access to Document

10.1109/ICL-GNSS54081.2022.9797032

Cite this

Subterranean positioning for a semi-autonomous robot supporting emergency task forces. / Reitbauer, Eva Maria; Schmied, Christoph; Didari Khamseh Motlagh, Hamid.
2022 International Conference on Localization and GNSS (ICL-GNSS). ed. / Jari Nurmi; Elena-Simona Lohan; Joaquin Torres Sospedra; Heidi Kuusniemi; Aleksandr Ometov. IEEE Xplore, 2022.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Reitbauer, EM, Schmied, C & Didari Khamseh Motlagh, H 2022, Subterranean positioning for a semi-autonomous robot supporting emergency task forces. in J Nurmi, E-S Lohan, JT Sospedra, H Kuusniemi & A Ometov (eds), 2022 International Conference on Localization and GNSS (ICL-GNSS). IEEE Xplore, 2022 International Conference on Localization and GNSS, Tampere, Finland, 7/06/22. https://doi.org/10.1109/ICL-GNSS54081.2022.9797032

@inproceedings{95bb7120223a47ba83d548b4ea4fa000,

title = "Subterranean positioning for a semi-autonomous robot supporting emergency task forces",

abstract = "This paper proposes a positioning algorithm for a semi-autonomous robot in subterranean scenarios. The robot is equipped with positioning sensors, imaging sensors, and sensors to detect hazardous materials. The sensors can be used to automatically generate a site map to increase safety for emergency forces. To create an accurate map, the position and attitude of the robot have to be determined. This is done using an extended Kalman filter which fuses data from LIDAR, wheel odometry, and a MEMS IMU. Tests were carried out in a tunnel in Eisenerz, Austria. To evaluate the achievable accuracy, the estimated position of the filter is compared to a ground truth. The results show that with the developed sensor fusion algorithm, a horizontal positioning error of 1.07% of the traveled distance can be achieved.",

keywords = "extended Kalman filtering, GICP, IMU, LIDAR, odometry, semi-autonomous robot, subterranean positioning",

author = "Reitbauer, {Eva Maria} and Christoph Schmied and {Didari Khamseh Motlagh}, Hamid",

year = "2022",

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doi = "10.1109/ICL-GNSS54081.2022.9797032",

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editor = "Jari Nurmi and Elena-Simona Lohan and Sospedra, {Joaquin Torres} and Heidi Kuusniemi and Aleksandr Ometov",

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AU - Reitbauer, Eva Maria

AU - Schmied, Christoph

AU - Didari Khamseh Motlagh, Hamid

PY - 2022/6

Y1 - 2022/6

N2 - This paper proposes a positioning algorithm for a semi-autonomous robot in subterranean scenarios. The robot is equipped with positioning sensors, imaging sensors, and sensors to detect hazardous materials. The sensors can be used to automatically generate a site map to increase safety for emergency forces. To create an accurate map, the position and attitude of the robot have to be determined. This is done using an extended Kalman filter which fuses data from LIDAR, wheel odometry, and a MEMS IMU. Tests were carried out in a tunnel in Eisenerz, Austria. To evaluate the achievable accuracy, the estimated position of the filter is compared to a ground truth. The results show that with the developed sensor fusion algorithm, a horizontal positioning error of 1.07% of the traveled distance can be achieved.

AB - This paper proposes a positioning algorithm for a semi-autonomous robot in subterranean scenarios. The robot is equipped with positioning sensors, imaging sensors, and sensors to detect hazardous materials. The sensors can be used to automatically generate a site map to increase safety for emergency forces. To create an accurate map, the position and attitude of the robot have to be determined. This is done using an extended Kalman filter which fuses data from LIDAR, wheel odometry, and a MEMS IMU. Tests were carried out in a tunnel in Eisenerz, Austria. To evaluate the achievable accuracy, the estimated position of the filter is compared to a ground truth. The results show that with the developed sensor fusion algorithm, a horizontal positioning error of 1.07% of the traveled distance can be achieved.

KW - extended Kalman filtering

KW - GICP

KW - IMU

KW - LIDAR

KW - odometry

KW - semi-autonomous robot

KW - subterranean positioning

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M3 - Conference paper

BT - 2022 International Conference on Localization and GNSS (ICL-GNSS)

A2 - Nurmi, Jari

A2 - Lohan, Elena-Simona

A2 - Sospedra, Joaquin Torres

A2 - Kuusniemi, Heidi

A2 - Ometov, Aleksandr

PB - IEEE Xplore

T2 - 2022 International Conference on Localization and GNSS

Y2 - 7 June 2022 through 9 June 2022

ER -

Subterranean positioning for a semi-autonomous robot supporting emergency task forces

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Conference

Keywords

ASJC Scopus subject areas

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