Data Fusion for Multipath-Based SLAM

E. Leitinger; Florian Meyer

doi:10.1109/IEEECONF51394.2020.9443537

Data Fusion for Multipath-Based SLAM

E. Leitinger, Florian Meyer

Institute of Signal Processing and Speech Communication (4420)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

Multipath-based simultaneous localization and mapping (SLAM) algorithms can detect and localize radio reflective surfaces and jointly estimate the time-varying position of mobile agents. A promising approach is to represent radio reflective surfaces by so called virtual anchors (VAs). In existing multipath-based SLAM algorithms, VAs are modeled and inferred for each physical anchor (PA) and each propagation path individually, even if multiple VAs represent the same physical surface. This limits timeliness and accuracy of mapping and agent localization. In this paper, we introduce an improved statistical model and estimation method that enables data fusion for multipath-based SLAM by representing each surface with a single master virtual anchor (MVA). Our numerical simulation results show that the proposed multipath-based SLAM algorithm can significantly increase map convergence speed and reduce the mapping error compared to a state-of-the-art metho

Original language	English
Title of host publication	2020 54th Asilomar Conference on Signals, Systems, and Computers
Place of Publication	Pacifc Grove, CA, USA
DOIs	https://doi.org/10.1109/IEEECONF51394.2020.9443537
Publication status	Published - 1 Oct 2020

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10.1109/IEEECONF51394.2020.9443537

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title = "Data Fusion for Multipath-Based SLAM",

abstract = "Multipath-based simultaneous localization and mapping (SLAM) algorithms can detect and localize radio reflective surfaces and jointly estimate the time-varying position of mobile agents. A promising approach is to represent radio reflective surfaces by so called virtual anchors (VAs). In existing multipath-based SLAM algorithms, VAs are modeled and inferred for each physical anchor (PA) and each propagation path individually, even if multiple VAs represent the same physical surface. This limits timeliness and accuracy of mapping and agent localization. In this paper, we introduce an improved statistical model and estimation method that enables data fusion for multipath-based SLAM by representing each surface with a single master virtual anchor (MVA). Our numerical simulation results show that the proposed multipath-based SLAM algorithm can significantly increase map convergence speed and reduce the mapping error compared to a state-of-the-art metho",

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AU - Meyer, Florian

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N2 - Multipath-based simultaneous localization and mapping (SLAM) algorithms can detect and localize radio reflective surfaces and jointly estimate the time-varying position of mobile agents. A promising approach is to represent radio reflective surfaces by so called virtual anchors (VAs). In existing multipath-based SLAM algorithms, VAs are modeled and inferred for each physical anchor (PA) and each propagation path individually, even if multiple VAs represent the same physical surface. This limits timeliness and accuracy of mapping and agent localization. In this paper, we introduce an improved statistical model and estimation method that enables data fusion for multipath-based SLAM by representing each surface with a single master virtual anchor (MVA). Our numerical simulation results show that the proposed multipath-based SLAM algorithm can significantly increase map convergence speed and reduce the mapping error compared to a state-of-the-art metho

AB - Multipath-based simultaneous localization and mapping (SLAM) algorithms can detect and localize radio reflective surfaces and jointly estimate the time-varying position of mobile agents. A promising approach is to represent radio reflective surfaces by so called virtual anchors (VAs). In existing multipath-based SLAM algorithms, VAs are modeled and inferred for each physical anchor (PA) and each propagation path individually, even if multiple VAs represent the same physical surface. This limits timeliness and accuracy of mapping and agent localization. In this paper, we introduce an improved statistical model and estimation method that enables data fusion for multipath-based SLAM by representing each surface with a single master virtual anchor (MVA). Our numerical simulation results show that the proposed multipath-based SLAM algorithm can significantly increase map convergence speed and reduce the mapping error compared to a state-of-the-art metho

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DO - 10.1109/IEEECONF51394.2020.9443537

M3 - Conference paper

BT - 2020 54th Asilomar Conference on Signals, Systems, and Computers

CY - Pacifc Grove, CA, USA

ER -

Data Fusion for Multipath-Based SLAM

Abstract

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