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A Probabilistic Moving Horizon Estimation Framework Applied to the Visual-Inertial Sensor Fusion Problem

Fiedler, Felix and Baumbach, Dirk and Börner, Anko and Lucia, Sergio (2020) A Probabilistic Moving Horizon Estimation Framework Applied to the Visual-Inertial Sensor Fusion Problem. In: European Control Conference 2020, ECC 2020, pp. 1009-1016. IEEE. 2020 European Control Conference (ECC), 2020-05-02 - 2020-05-15, Saint Petersburg, Russia. doi: 10.23919/ECC51009.2020.9143645. ISBN 978-390714401-5.

Full text not available from this repository.

Official URL: https://ieeexplore.ieee.org/document/9143645

Abstract

We propose a novel method to compute the arrival cost for the moving horizon estimator. The choice of the arrival cost is an important challenge and is known to have significant influence on the performance of the estimator. Most common approaches are based on implementing a complementary extended Kalman filter to propagate an approximate measure of the uncertainty. Our approach is based on the probabilistic interpretation of the moving horizon estimator and its analogy to the maximum a posteriori estimator. We derive a method to directly obtain the required uncertainties from the Hessian of the moving horizon estimation objective function. We showcase our novel approach with the challenging visual-inertial sensor fusion problem that commonly arises in visual navigation systems. The estimation performance is significantly better compared to our previous results based on the extended Kalman filter. Additionally, the proposed algorithm calibrates the inertial sensor online and is immediately ready for operation.

Item URL in elib:https://elib.dlr.de/136791/
Document Type:Conference or Workshop Item (Speech)
Title:A Probabilistic Moving Horizon Estimation Framework Applied to the Visual-Inertial Sensor Fusion Problem
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Fiedler, FelixUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Baumbach, DirkUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Börner, AnkoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Lucia, SergioUNSPECIFIEDhttps://orcid.org/0000-0002-3347-5593UNSPECIFIED
Date:2020
Journal or Publication Title:European Control Conference 2020, ECC 2020
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
DOI:10.23919/ECC51009.2020.9143645
Page Range:pp. 1009-1016
Publisher:IEEE
ISBN:978-390714401-5
Status:Published
Keywords:Hessian matrices;image filtering;inertial navigation;Kalman filters;maximum likelihood estimation;nonlinear filters;sensor fusion;Hessian uncertainty;visual navigation systems;probabilistic moving horizon estimation framework applied;visual-inertial sensor fusion problem;moving horizon estimation objective function;maximum a posteriori estimator;extended Kalman filter;Probabilistic logic;Sensor fusion;Estimation;Uncertainty;Visualization;Probability density function;Kalman filters
Event Title:2020 European Control Conference (ECC)
Event Location:Saint Petersburg, Russia
Event Type:international Conference
Event Start Date:2 May 2020
Event End Date:15 May 2020
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben Optische Sensorik - Theorie, Kalibration, Verifikation (old), V - D.MoVe (old)
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Optical Sensor Systems > Real-Time Data Processing
Deposited By: Baumbach, Dirk
Deposited On:26 Oct 2020 12:25
Last Modified:24 Apr 2024 20:39

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