The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Camera-IMU-based localization: Observability analysis and consistency improvement
Abstract
This work investigates the relationship between system observability properties and estimator inconsistency for a Vision-aided Inertial Navigation System (VINS). In particular, first we introduce a new methodology for determining the unobservable directions of nonlinear systems by factorizing the observability matrix according to the observable and unobservable modes. Subsequently, we apply this method to the VINS nonlinear model and determine its unobservable directions analytically. We leverage our analysis to improve the accuracy and consistency of linearized estimators applied to VINS. Our key findings are evaluated through extensive simulations and experimental validation on real-world data, demonstrating the superior accuracy and consistency of the proposed VINS framework compared to standard approaches.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The performance mismatch between filtering and MAP estimation gets smaller when the linearization point for the EKF is accurate, when using sliding-window filters, and when potential sources of inconsistency in the EKF are taken care of.
“Not surprisingly, the performance mismatch between filtering and MAP estimation gets smaller when the linearization point for the EKF is accurate (as it happens in visual-inertial navigation problems), when using slidingwindow filters, and when potential sources of inconsistency in the EKF are taken care of [105, 108, 139].”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [105]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Shi, J., & Tomasi (1994) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94 · doi:10.1109/cvpr.1994.323794 · Cited by 7,724 · cited 1×
“Short-term features are extracted from images using the Shi–Tomasi corner detector (Shi and Tomasi 1994).”
MethodNonlinear controllability and observability
Hermann, R., & Krener, A. (1977) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1977.1101601 · Cited by 2,374 · cited 9×
“In this section, we provide a brief overview of the method in Hermann and Krener (1977) for studying the observability of nonlinear systems and then introduce a new methodology for determining its unobservable directions.”
BackgroundMethodInfluential
Flagged first, then by citations
Research works printed but not linked: 36
- 10.1002/0471221279 · DOI · Paper page
- 10.1017/CBO9780511804441 · DOI · Paper page
- 10.1109/TAES.2008.4517003 · DOI
- Carlone L, 2012, Proceedings of the International symposium on artificial intelligence, robotics and automation in space, 7
- 10.2514/4.866463 · DOI
- 10.1109/34.993559 · DOI
- 10.1109/ROBOT.2009.5152778 · DOI
- Hesch JA, 2012, International workshop on the algorithmic foundations of robotics, 559
- 10.1109/ROBOT.2010.5509693 · DOI
- Huang GP, 2008, Proceedings of the International symposium on experimental robotics, 373
- 10.1177/0278364909353640 · DOI
- 10.1007/s10514-010-9207-y · DOI
- 10.1007/978-3-662-02581-9 · DOI
- 10.1177/0278364910388963 · DOI
- 10.1177/0278364910382802 · DOI
- 10.1016/j.robot.2006.06.006 · DOI
- 10.5244/C.25.16 · DOI
- 10.1109/ICRA.2012.6225229 · DOI
- 10.1177/0278364913481251 · DOI · Paper page
- Lucas B, 1981, Proceedings of the International joint conference on artificial intelligence, 674
- 10.1109/TRO.2011.2170332 · DOI
- 10.1109/TRO.2011.2160468 · DOI
- Maybeck PS, 1979, Stochastic models, estimation, and control, 1
- 10.1137/1.9780898719512 · DOI · Paper page
- 10.1109/TRO.2008.2004486 · DOI
- 10.1109/ROBOT.2007.364024 · DOI · Paper page
- 10.1109/CVPR.2003.1211470 · DOI · Paper page
- 10.1109/ROBOT.2002.1014801 · DOI
- 10.1109/ICRA.2011.5980357 · DOI
- Shi J, 1994, Proceedings of the IEEE conference on computer vision and pattern recognition, 593
- Shuster MD, 1993, Journal of the Astronautical Sciences, 41, 439
- 10.1177/0278364904045593 · DOI
- Weiss S, 2012, Vision based navigation for micro helicopters
- 10.1109/ICRA.2012.6225002 · DOI
- 10.1109/ICRA.2011.5979997 · DOI
- 10.1109/ICRA.2011.5980291 · DOI
Cited by
Sorted by citations
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry
Forster, C., Carlone, L., Dellaert, F., & Scaramuzza, D. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2597321 · Cited by 1,502
Metadata
- Authors
- Hesch, J. A., Kottas, D. G., Bowman, S. L., & Roumeliotis, S. I.
- Affiliations
- Google (United States) (US)
- University of Minnesota (US)
- University of Pennsylvania (US)
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2013-11-13
- Volume
- 33
- Issue
- 1
- Pages
- 182-201
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2056298239
- Cited by
- 245
- References
- 2
- Citations by year
- 2026: 10 · 2025: 10 · 2024: 16 · 2023: 14 · 2022: 20 · 2021: 12 · 2020: 19 · 2019: 28 · 2018: 33 · 2017: 33 · 2016: 23 · 2015: 21 · 2014: 6
- FWCI
- 26.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The International Journal of Robotics Research (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Keywords
- vision-aided inertial navigation, observability analysis, estimator inconsistency
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
