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

Fiedler, Felix; Baumbach, Dirk; Börner, Anko; Lucia, Sergio

doi:10.23919/ecc51009.2020.9143645

Cited by 4 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of the multi-rate MHE, family of filtering methods such as extended KF (Rao and Rawlings, 2002), unscented KF (Qu and Hahn, 2009), QR decomposition (Diehl et al, 2006) are utilized for the arrival cost computation. Nonlinear programming sensitivity is used to approximate the smoothed covariance with the inverse of the Hessian of the objective function (López-Negrete and Biegler, 2012;Fiedler et al, 2020).…”

Section: Multi-rate Measurements and Ill-conditioning Of The Estimati...mentioning

confidence: 99%

Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}

Kim¹,

Krausch²,

Aizpuru³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

We discuss the application of a nonlinear model predictive control (MPC) and a moving horizon estimation (MHE) to achieve an optimal operation of E. coli fed-batch cultivations with intermittent bolus feeding. 24 parallel experiments were considered in a high-throughput microbioreactor platform at 10 mL scale. The robotic island in question can run up to 48 fed-batch processes in parallel with an automated liquid handling and online and atline analytics. The implementation of the model-based monitoring and control framework reveals that there are mainly three challenges which need to be addressed; First, the inputs are given in an instantaneous pulsed form by bolus injections, second, online and atline measurement frequencies are severely imbalanced, and third, optimization for the distinctive multiple reactors can

show abstract

Section: Multi-rate Measurements and Ill-conditioning Of The Estimati...mentioning

confidence: 99%

Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}

Kim¹,

Krausch²,

Aizpuru³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Solving Problem (18) implies high computational cost if t f is large. For this reason, one could try to transform Problem (18) into a Moving Horizon one leading for t ≥ 0 and T > 0, to 19) has recently been numerically solved in several contexts in the literature, see [14,24,25,26,28,29,31,42]. However, the observavilty theory related to Problem (19) seems to have been overlooked.…”

Section: General Setupmentioning

confidence: 99%

“…The problem of SLAM consists of reconstructing the state of a robot and a map of its environment at the same time from partial measurements. Note that SLAM is more and more treated using optimization-based methods, see [8] and [21] and that MHE schemes have been recently tried in this context see [14,24,25,26,28,29,31,42]. However, the suitable observability conditions for fast MHE applied to SLAM does not seem to have been derived.…”

Section: Introductionmentioning

confidence: 99%

Non-uniform Observability for Moving Horizon Estimation and stability with respect to additive perturbation

Flayac¹,

Shames²

2021

Preprint

View full text Add to dashboard Cite

This paper first formalises a new observability concept, called weak regular observability, that is adapted to Fast Moving Horizon Estimation where one aims to estimate the state of a nonlinear system efficiently on rolling time windows in the case of small initial error. Additionally, sufficient conditions of weak regular observability are provided in a problem of Simultaneous Localisation and Mapping (SLAM) for different measurement models. In particular it is shown that following circular trajectories leads to weak regular observability in a second order 2D SLAM problem with several possible types of sensors.

show abstract

“…The interested reader is referred to the special issue (Alessandri and Battistelli, 2020) for recent advances on MHE. In a Bayesian framework, MHE can be conveniently exploited to approximate the full-information Bayesian estimation problem whenever the latter does not admit a closed-form recursive solution (Delgado and Goodwin, 2014;Fiedler et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Variational Bayes Moving Horizon Estimation Adaptive Filter with Guaranteed Stability

Dong¹,

Battistelli²,

Chisci³

et al. 2021

Preprint

View full text Add to dashboard Cite

This paper addresses state estimation of linear systems with special attention on unknown process and measurement noise covariances, aiming to enhance estimation accuracy while preserving the stability guarantee of the Kalman filter. To this end, the full information estimation problem over a finite interval is firstly addressed. Then, a novel adaptive variational Bayesian (VB) moving horizon estimation (MHE) method is proposed, exploiting VB inference, MHE and Monte Carlo integration with importance sampling for joint estimation of the unknown process and measurement noise covariances, along with the state trajectory over a moving window of fixed length. Further, it is proved that the proposed adaptive VB MHE filter ensures mean-square boundedness of the estimation error with any number of importance samples and VB iterations, as well as for any window length. Finally, simulation results on a target tracking example demonstrate the effectiveness of the VB MHE filter with enhanced estimation accuracy and convergence properties compared to the conventional non-adaptive Kalman filter and other existing adaptive filters.

show abstract

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

Cited by 4 publications

References 0 publications

Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}

Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}

Non-uniform Observability for Moving Horizon Estimation and stability with respect to additive perturbation

A Variational Bayes Moving Horizon Estimation Adaptive Filter with Guaranteed Stability

Contact Info

Product

Resources

About