Daun, Kevin ; Schnaubelt, Marius ; Kohlbrecher, Stefan ; Stryk, Oskar von (2021)
HectorGrapher: Continuous-time Lidar SLAM with Multi-resolution Signed Distance Function Registration for Challenging Terrain.
2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). New York, USA (25.10.2021-27.10.2021)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
For deployment in previously unknown, unstructured, and GPS-denied environments, autonomous mobile rescue robots need to localize themselves in such environments and create a map of it using a simultaneous localization and mapping (SLAM) approach. Continuous-time SLAM approaches represent the pose as a time-continuous estimate that provides high accuracy and allows correcting for distortions induced by motion during the scan capture. To enable robust and accurate real-time SLAM in challenging terrain, we propose HectorGrapher which enables accurate localization by continuous-time pose estimation and robust scan registration based on multi-resolution signed distance functions. We evaluate the method in multiple publicly available real-world datasets, as well as a data set from the RoboCup 2021 Rescue League, where we applied the proposed method to win the Best-in-Class "Exploration and Mapping" Award.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Daun, Kevin ; Schnaubelt, Marius ; Kohlbrecher, Stefan ; Stryk, Oskar von |
| Art des Eintrags: | Bibliographie |
| Titel: | HectorGrapher: Continuous-time Lidar SLAM with Multi-resolution Signed Distance Function Registration for Challenging Terrain |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Veranstaltungstitel: | 2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) |
| Veranstaltungsort: | New York, USA |
| Veranstaltungsdatum: | 25.10.2021-27.10.2021 |
| Kurzbeschreibung (Abstract): | For deployment in previously unknown, unstructured, and GPS-denied environments, autonomous mobile rescue robots need to localize themselves in such environments and create a map of it using a simultaneous localization and mapping (SLAM) approach. Continuous-time SLAM approaches represent the pose as a time-continuous estimate that provides high accuracy and allows correcting for distortions induced by motion during the scan capture. To enable robust and accurate real-time SLAM in challenging terrain, we propose HectorGrapher which enables accurate localization by continuous-time pose estimation and robust scan registration based on multi-resolution signed distance functions. We evaluate the method in multiple publicly available real-world datasets, as well as a data set from the RoboCup 2021 Rescue League, where we applied the proposed method to win the Best-in-Class "Exploration and Mapping" Award. |
| Freie Schlagworte: | emergenCITY_CPS |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Simulation, Systemoptimierung und Robotik LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > emergenCITY |
| TU-Projekte: | Bund/BMBF|13N14861|A-DRZ HMWK|III L6-519/03/05.001-(0016)|emergenCity TP Bock |
| Hinterlegungsdatum: | 11 Nov 2021 07:15 |
| Letzte Änderung: | 26 Aug 2022 10:10 |
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