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Robust Multisensor Fusion for Reliable Mapping and Navigation in Degraded Visual Conditions

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.-27.10.2021, [Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2021
Creators: Torchalla, Moritz ; Schnaubelt, Marius ; Daun, Kevin ; Stryk, Oskar von
Title: Robust Multisensor Fusion for Reliable Mapping and Navigation in Degraded Visual Conditions
Language: English
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.

Uncontrolled Keywords: emergenCITY_CPS
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Simulation, Systems Optimization and Robotics Group
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > emergenCITY
TU-Projects: Bund/BMBF|13N14861|A-DRZ
HMWK|III L6-519/03/05.001-(0016)|emergenCity TP Bock
Event Title: 2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)
Event Location: New York, USA
Event Dates: 25.-27.10.2021
Date Deposited: 11 Nov 2021 07:17
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