Torchalla, Moritz ; Schnaubelt, Marius ; Daun, Kevin ; Stryk, Oskar von (2021)
Robust Multisensor Fusion for Reliable Mapping and Navigation in Degraded Visual Conditions.
2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). New York, USA (25.10.2021-27.10.2021)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
We address the problem of robust simultaneous mapping and localization in degraded visual conditions using low-cost off-the-shelf radars. Current methods often use high- end radar sensors or are tightly coupled to specific sensors, limiting the applicability to new robots. In contrast, we present a sensor-agnostic processing pipeline based on a novel forward sensor model to achieve accurate updates of signed distance function-based maps and robust optimization techniques to reach robust and accurate pose estimates. Our evaluation demonstrates accurate mapping and pose estimation in indoor environments under poor visual conditions and higher accuracy compared to existing methods on publicly available benchmark data.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Torchalla, Moritz ; Schnaubelt, Marius ; Daun, Kevin ; Stryk, Oskar von |
| Art des Eintrags: | Bibliographie |
| Titel: | Robust Multisensor Fusion for Reliable Mapping and Navigation in Degraded Visual Conditions |
| 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): | We address the problem of robust simultaneous mapping and localization in degraded visual conditions using low-cost off-the-shelf radars. Current methods often use high- end radar sensors or are tightly coupled to specific sensors, limiting the applicability to new robots. In contrast, we present a sensor-agnostic processing pipeline based on a novel forward sensor model to achieve accurate updates of signed distance function-based maps and robust optimization techniques to reach robust and accurate pose estimates. Our evaluation demonstrates accurate mapping and pose estimation in indoor environments under poor visual conditions and higher accuracy compared to existing methods on publicly available benchmark data. |
| 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 07:17 |
| Letzte Änderung: | 26 Aug 2022 10:18 |
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