Details
| Original language | English |
|---|---|
| Title of host publication | Informatics in Control, Automation and Robotics - 19th International Conference, ICINCO 2022, Revised Selected Papers |
| Subtitle of host publication | Informatics in Control, Automation and Robotics ICINCO 2022 |
| Editors | Giuseppina Gini, Henk Nijmeijer, Wolfram Burgard, Dimitar Filev |
| Pages | 44–64 |
| Number of pages | 21 |
| ISBN (Electronic) | 978-3-031-48303-5 |
| Publication status | Published - 2023 |
Publication series
| Name | Lecture Notes in Networks and Systems |
|---|---|
| Volume | 836 LNNS |
| ISSN (Print) | 2367-3370 |
| ISSN (Electronic) | 2367-3389 |
Abstract
Sensor fusion in mobile robots requires proper extrinsic and intrinsic sensor calibration. Robots in the search and rescue robotics domain are often equipped with multiple range sensor modalities such as radar and lidar due to the harsh environmental conditions. This article presents a method to easily calibrate a 2D scanning radar and a 3D lidar without the use of special calibration targets. Therefore, it focuses on the improvement of the feature extraction from the environment by applying filtering algorithms to remove noise and improve the signal-to-noise ratio. Additionally, a second optimization stage is introduced to propagate measurement uncertainties of the lidar to the calibration result. The results are compared to the previous version of the algorithm as well as to the ground truth parameters. Furthermore, statistical tests are performed to confirm the validity of the calibration results.
Keywords
- 2D scanning radar, 3D lidar, Mobile robotics, Search and rescue robotics, Target-less calibration
ASJC Scopus subject areas
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Networks and Communications
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Informatics in Control, Automation and Robotics - 19th International Conference, ICINCO 2022, Revised Selected Papers: Informatics in Control, Automation and Robotics ICINCO 2022. ed. / Giuseppina Gini; Henk Nijmeijer; Wolfram Burgard; Dimitar Filev. 2023. p. 44–64 (Lecture Notes in Networks and Systems; Vol. 836 LNNS).
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - Improving 2D Scanning Radar and 3D Lidar Calibration
AU - Rotter, Jan M.
AU - Stanke, Levin
AU - Wagner, Bernardo
N1 - This work has partly been funded by the German Federal Ministry of Education and Research (BMBF) under the project number 13N15550 (UAV-Rescue).
PY - 2023
Y1 - 2023
N2 - Sensor fusion in mobile robots requires proper extrinsic and intrinsic sensor calibration. Robots in the search and rescue robotics domain are often equipped with multiple range sensor modalities such as radar and lidar due to the harsh environmental conditions. This article presents a method to easily calibrate a 2D scanning radar and a 3D lidar without the use of special calibration targets. Therefore, it focuses on the improvement of the feature extraction from the environment by applying filtering algorithms to remove noise and improve the signal-to-noise ratio. Additionally, a second optimization stage is introduced to propagate measurement uncertainties of the lidar to the calibration result. The results are compared to the previous version of the algorithm as well as to the ground truth parameters. Furthermore, statistical tests are performed to confirm the validity of the calibration results.
AB - Sensor fusion in mobile robots requires proper extrinsic and intrinsic sensor calibration. Robots in the search and rescue robotics domain are often equipped with multiple range sensor modalities such as radar and lidar due to the harsh environmental conditions. This article presents a method to easily calibrate a 2D scanning radar and a 3D lidar without the use of special calibration targets. Therefore, it focuses on the improvement of the feature extraction from the environment by applying filtering algorithms to remove noise and improve the signal-to-noise ratio. Additionally, a second optimization stage is introduced to propagate measurement uncertainties of the lidar to the calibration result. The results are compared to the previous version of the algorithm as well as to the ground truth parameters. Furthermore, statistical tests are performed to confirm the validity of the calibration results.
KW - 2D scanning radar
KW - 3D lidar
KW - Mobile robotics
KW - Search and rescue robotics
KW - Target-less calibration
UR - http://www.scopus.com/inward/record.url?scp=85180149014&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-48303-5_3
DO - 10.1007/978-3-031-48303-5_3
M3 - Contribution to book/anthology
SN - 978-3-031-48302-8
T3 - Lecture Notes in Networks and Systems
SP - 44
EP - 64
BT - Informatics in Control, Automation and Robotics - 19th International Conference, ICINCO 2022, Revised Selected Papers
A2 - Gini, Giuseppina
A2 - Nijmeijer, Henk
A2 - Burgard, Wolfram
A2 - Filev, Dimitar
ER -