Details
| Original language | English |
|---|---|
| Title of host publication | 2020 IEEE International Conference on Robotics and Automation, ICRA 2020 |
| Pages | 9652-9658 |
| Number of pages | 7 |
| ISBN (electronic) | 9781728173955 |
| Publication status | Published - 2020 |
Abstract
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Artificial Intelligence
- Engineering(all)
- Electrical and Electronic Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
2020 IEEE International Conference on Robotics and Automation, ICRA 2020. 2020. p. 9652-9658 9196997.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Map Management Approach for SLAM in Large-Scale Indoor and Outdoor Areas
AU - Ehlers, Simon Friedrich Gerhard
AU - Stuede, Marvin
AU - Nülle, Kathrin
AU - Ortmaier, Tobias
PY - 2020
Y1 - 2020
N2 - This work presents a semantic map management approach for various environments by triggering multiple maps with different simultaneous localization and mapping (SLAM) configurations. A modular map structure allows to add, modify or delete maps without influencing other maps of different areas. The hierarchy level of our algorithm is above the utilized SLAM method. Evaluating laser scan data (e.g. the detection of passing a doorway) triggers a new map, automatically choosing the appropriate SLAM configuration from a manually predefined list. Single independent maps are connected by link-points, which are located in an overlapping zone of both maps, enabling global navigation over several maps. Loop- closures between maps are detected by an appearance-based method, using feature matching and iterative closest point (ICP) registration between point clouds. Based on the arrangement of maps and link-points, a topological graph is extracted for navigation purpose and tracking the global robot's position over several maps. Our approach is evaluated by mapping a university campus with multiple indoor and outdoor areas and abstracting a metrical-topological graph. It is compared to a single map running with different SLAM configurations. Our approach enhances the overall map quality compared to the single map approaches by automatically choosing predefined SLAM configurations for different environmental setups.
AB - This work presents a semantic map management approach for various environments by triggering multiple maps with different simultaneous localization and mapping (SLAM) configurations. A modular map structure allows to add, modify or delete maps without influencing other maps of different areas. The hierarchy level of our algorithm is above the utilized SLAM method. Evaluating laser scan data (e.g. the detection of passing a doorway) triggers a new map, automatically choosing the appropriate SLAM configuration from a manually predefined list. Single independent maps are connected by link-points, which are located in an overlapping zone of both maps, enabling global navigation over several maps. Loop- closures between maps are detected by an appearance-based method, using feature matching and iterative closest point (ICP) registration between point clouds. Based on the arrangement of maps and link-points, a topological graph is extracted for navigation purpose and tracking the global robot's position over several maps. Our approach is evaluated by mapping a university campus with multiple indoor and outdoor areas and abstracting a metrical-topological graph. It is compared to a single map running with different SLAM configurations. Our approach enhances the overall map quality compared to the single map approaches by automatically choosing predefined SLAM configurations for different environmental setups.
UR - http://www.scopus.com/inward/record.url?scp=85092721369&partnerID=8YFLogxK
U2 - 10.15488/10360
DO - 10.15488/10360
M3 - Conference contribution
SN - 978-1-7281-7394-8
SN - 978-1-7281-7396-2
SP - 9652
EP - 9658
BT - 2020 IEEE International Conference on Robotics and Automation, ICRA 2020
ER -