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

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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 languageEnglish
Title of host publication2022 International Conference on Localization and GNSS (ICL-GNSS)
EditorsJari Nurmi, Elena-Simona Lohan, Joaquin Torres Sospedra, Heidi Kuusniemi, Aleksandr Ometov
PublisherIEEE Xplore
Number of pages7
ISBN (Electronic)9781665405751
Publication statusPublished - Jun 2022
Event2022 International Conference on Localization and GNSS: ICL-GNSS 2022 - Tampere, Finland
Duration: 7 Jun 20229 Jun 2022


Conference2022 International Conference on Localization and GNSS
Abbreviated titleICL-GNSS 2022


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

ASJC Scopus subject areas

  • Aerospace Engineering
  • Computer Networks and Communications


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