Optimization-Based VINS: Consistency, Marginalization, and FEJ

In this work, we present a comprehensive analysis of the application of the First-estimates Jacobian (FEJ) design methodology in nonlinear optimization-based Visual-Inertial Navigation Systems (VINS). The FEJ approach fixes system linearization points to preserve proper observability properties of V...

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Published in:Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 1517 - 1524
Main Authors: Chen, Chuchu, Geneva, Patrick, Peng, Yuxiang, Lee, Woosik, Huang, Guoquan
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2023
Subjects:
Design methodology
Estimation
Guidelines
Intelligent robots
Jacobian matrices
Navigation
Observability
ISSN:2153-0866
Online Access:Get full text
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Abstract In this work, we present a comprehensive analysis of the application of the First-estimates Jacobian (FEJ) design methodology in nonlinear optimization-based Visual-Inertial Navigation Systems (VINS). The FEJ approach fixes system linearization points to preserve proper observability properties of VINS and has been shown to significantly improve the estimation performance of state-of-the-art filtering-based methods. However, its direct application to optimization-based estimators holds challenges and pitfalls, which we addressed in this paper. Specifically, we carefully examine the observability and its relation to inconsistency and FEJ, based on this, we explain how to properly apply and implement FEJ within four marginalization archetypes commonly used in non-linear optimizationbased frameworks. FEJ's effectiveness and applications to VINS are investigated and demonstrate significant performance improvements. Additionally, we offer a detailed discussion of results and guidelines on how to properly implement FEJ in optimization-based estimators.
AbstractList In this work, we present a comprehensive analysis of the application of the First-estimates Jacobian (FEJ) design methodology in nonlinear optimization-based Visual-Inertial Navigation Systems (VINS). The FEJ approach fixes system linearization points to preserve proper observability properties of VINS and has been shown to significantly improve the estimation performance of state-of-the-art filtering-based methods. However, its direct application to optimization-based estimators holds challenges and pitfalls, which we addressed in this paper. Specifically, we carefully examine the observability and its relation to inconsistency and FEJ, based on this, we explain how to properly apply and implement FEJ within four marginalization archetypes commonly used in non-linear optimizationbased frameworks. FEJ's effectiveness and applications to VINS are investigated and demonstrate significant performance improvements. Additionally, we offer a detailed discussion of results and guidelines on how to properly implement FEJ in optimization-based estimators.
Author Lee, Woosik
Chen, Chuchu
Geneva, Patrick
Peng, Yuxiang
Huang, Guoquan
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  givenname: Patrick
  surname: Geneva
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  givenname: Yuxiang
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  givenname: Woosik
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  givenname: Guoquan
  surname: Huang
  fullname: Huang, Guoquan
  email: ghuang@udel.edu
  organization: University of Delaware,Robot Perception and Navigation Group (RPNG),Newark,DE,USA,19716
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Snippet In this work, we present a comprehensive analysis of the application of the First-estimates Jacobian (FEJ) design methodology in nonlinear optimization-based...
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SubjectTerms Design methodology
Estimation
Guidelines
Intelligent robots
Jacobian matrices
Navigation
Observability
Title Optimization-Based VINS: Consistency, Marginalization, and FEJ
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