Daun, Kevin ; Kohlbrecher, Stefan ; Sturm, Jürgen ; Stryk, Oskar von (2019)
Large Scale 2D Laser SLAM using Truncated Signed Distance Functions.
IEEE International Symposium on Safety, Security, and Rescue Robotics. Würzburg, Germany (02.09.2019-04.09.2019)
doi: 10.1109/SSRR.2019.8848964
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
For deployment in previously unknown, unstructured and GPS-denied environments, autonomous mobile rescue robots need to localize themselves in the environment and create a map of it using a simultaneous localization and mapping (SLAM) approach. While most existing lidar-based methods use occupancy grids to represent a map, the use of truncated signed distance functions (TSDFs) is investigated in this paper to improve accuracy and robustness. In contrast to occupancy grids, TSDFs represent the distance to the nearest surface in every grid cell. This enables sub-pixel precision during localization and increases the basin of convergence of scan matching. To enable consistent mapping of large spaces, an efficient branch-and-bound based loop closure detection is applied. The evaluation of the proposed approach with publicly available benchmark data shows that the proposed approach yields improved accuracy in comparison to occupancy grid based methods, while requiring similar runtime. Furthermore, it is demonstrated that the proposed approach is able to map a large scale environment with urban search and rescue elements in real-time.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Daun, Kevin ; Kohlbrecher, Stefan ; Sturm, Jürgen ; Stryk, Oskar von |
| Art des Eintrags: | Bibliographie |
| Titel: | Large Scale 2D Laser SLAM using Truncated Signed Distance Functions |
| Sprache: | Englisch |
| Publikationsjahr: | 26 September 2019 |
| Verlag: | IEEE |
| Buchtitel: | 2019 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR 2019) |
| Veranstaltungstitel: | IEEE International Symposium on Safety, Security, and Rescue Robotics |
| Veranstaltungsort: | Würzburg, Germany |
| Veranstaltungsdatum: | 02.09.2019-04.09.2019 |
| DOI: | 10.1109/SSRR.2019.8848964 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | For deployment in previously unknown, unstructured and GPS-denied environments, autonomous mobile rescue robots need to localize themselves in the environment and create a map of it using a simultaneous localization and mapping (SLAM) approach. While most existing lidar-based methods use occupancy grids to represent a map, the use of truncated signed distance functions (TSDFs) is investigated in this paper to improve accuracy and robustness. In contrast to occupancy grids, TSDFs represent the distance to the nearest surface in every grid cell. This enables sub-pixel precision during localization and increases the basin of convergence of scan matching. To enable consistent mapping of large spaces, an efficient branch-and-bound based loop closure detection is applied. The evaluation of the proposed approach with publicly available benchmark data shows that the proposed approach yields improved accuracy in comparison to occupancy grid based methods, while requiring similar runtime. Furthermore, it is demonstrated that the proposed approach is able to map a large scale environment with urban search and rescue elements in real-time. |
| Freie Schlagworte: | optical radar;rescue robots;SLAM (robots);tree searching;truncated signed distance functions;autonomous mobile rescue robots;grid cell;occupancy grid based methods;TSDF;unknown unstructured GPS-denied environments;lidar-based methods;consistent mapping approach;simultaneous localization and mapping approach;large scale 2D laser SLAM approach;efficient branch-and-bound based loop closure detection;urban search and rescue elements;Simultaneous localization and mapping;Real-time systems;Two dimensional displays;Optimization;Lasers;Robustness |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Simulation, Systemoptimierung und Robotik |
| Hinterlegungsdatum: | 03 Dez 2021 11:56 |
| Letzte Änderung: | 03 Dez 2021 11:56 |
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