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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| Vydané v: | Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems s. 1517 - 1524 |
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01.10.2023
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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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Chuchu surname: Chen fullname: Chen, Chuchu email: ccchu@udel.edu organization: University of Delaware,Robot Perception and Navigation Group (RPNG),Newark,DE,USA,19716 – sequence: 2 givenname: Patrick surname: Geneva fullname: Geneva, Patrick email: pgeneva@udel.edu organization: University of Delaware,Robot Perception and Navigation Group (RPNG),Newark,DE,USA,19716 – sequence: 3 givenname: Yuxiang surname: Peng fullname: Peng, Yuxiang email: yxpeng@udel.edu organization: University of Delaware,Robot Perception and Navigation Group (RPNG),Newark,DE,USA,19716 – sequence: 4 givenname: Woosik surname: Lee fullname: Lee, Woosik email: woosik@udel.edu organization: University of Delaware,Robot Perception and Navigation Group (RPNG),Newark,DE,USA,19716 – sequence: 5 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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| SubjectTerms | Design methodology Estimation Guidelines Intelligent robots Jacobian matrices Navigation Observability |
| Title | Optimization-Based VINS: Consistency, Marginalization, and FEJ |
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