Observability-Informed Measurement Validation for Visual-Inertial Navigation. Boler, M. May 2022. Accepted: 2022-05-04T16:19:36Z![Observability-Informed Measurement Validation for Visual-Inertial Navigation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex This thesis presents a measurement validation method for visual-inertial navigation and an implementation of a visual-inertial estimator which makes use of it. Many autonomous plat- forms, especially flying ones, rely on accurate and reliable state estimates from a visual-inertial estimator to maintain safe and controlled flight towards a goal. The measurement validation method presented in this thesis makes use of a geometric analysis of the landmark measuremnt model to enable early and reliable validation, safely integrating high-quality measurements into the state estimation process. First, the IMU and camera sensors are detailed along with the sensor processing techniques necessary to make use of them. Next, a detailed description of two standard visual-inertial estimation approaches is presented to develop necessary back- ground knowledge. Following these descriptions, an analysis of the relationships between the geometry of landmark observation and the accuracy and reliability of landmark estimates is per- formed, concluding with the proposal of a new validation method which delays measurement processing until the landmark is predicted to be observable. Lastly, a visual-inertial estimator is developed which makes use of the proposed method and tested on the EUROC dataset, the most common visual-inertial dataset, against several state-of-the-art estimators. In this comparison, the proposed estimator demonstrates competitive performace, reliably producing positioning errors of less than 0.5 meters over flights up to 120 meters long. Overall, the proposed method is demonstrated to be reliable and accurate in competition with significantly more advanced and complicated estimators.
@unpublished{boler_observability-informed_2022,
title = {Observability-{Informed} {Measurement} {Validation} for {Visual}-{Inertial} {Navigation}},
url = {https://etd.auburn.edu//handle/10415/8218},
abstract = {This thesis presents a measurement validation method for visual-inertial navigation and an
implementation of a visual-inertial estimator which makes use of it. Many autonomous plat-
forms, especially flying ones, rely on accurate and reliable state estimates from a visual-inertial
estimator to maintain safe and controlled flight towards a goal. The measurement validation
method presented in this thesis makes use of a geometric analysis of the landmark measuremnt
model to enable early and reliable validation, safely integrating high-quality measurements
into the state estimation process. First, the IMU and camera sensors are detailed along with
the sensor processing techniques necessary to make use of them. Next, a detailed description
of two standard visual-inertial estimation approaches is presented to develop necessary back-
ground knowledge. Following these descriptions, an analysis of the relationships between the
geometry of landmark observation and the accuracy and reliability of landmark estimates is per-
formed, concluding with the proposal of a new validation method which delays measurement
processing until the landmark is predicted to be observable. Lastly, a visual-inertial estimator is
developed which makes use of the proposed method and tested on the EUROC dataset, the most
common visual-inertial dataset, against several state-of-the-art estimators. In this comparison,
the proposed estimator demonstrates competitive performace, reliably producing positioning
errors of less than 0.5 meters over flights up to 120 meters long. Overall, the proposed method
is demonstrated to be reliable and accurate in competition with significantly more advanced
and complicated estimators.},
language = {en},
urldate = {2024-06-25},
author = {Boler, Matthew},
month = may,
year = {2022},
note = {Accepted: 2022-05-04T16:19:36Z},
}
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First, the IMU and camera sensors are detailed along with the sensor processing techniques necessary to make use of them. Next, a detailed description of two standard visual-inertial estimation approaches is presented to develop necessary back- ground knowledge. Following these descriptions, an analysis of the relationships between the geometry of landmark observation and the accuracy and reliability of landmark estimates is per- formed, concluding with the proposal of a new validation method which delays measurement processing until the landmark is predicted to be observable. Lastly, a visual-inertial estimator is developed which makes use of the proposed method and tested on the EUROC dataset, the most common visual-inertial dataset, against several state-of-the-art estimators. In this comparison, the proposed estimator demonstrates competitive performace, reliably producing positioning errors of less than 0.5 meters over flights up to 120 meters long. Overall, the proposed method is demonstrated to be reliable and accurate in competition with significantly more advanced and complicated estimators.","language":"en","urldate":"2024-06-25","author":[{"propositions":[],"lastnames":["Boler"],"firstnames":["Matthew"],"suffixes":[]}],"month":"May","year":"2022","note":"Accepted: 2022-05-04T16:19:36Z","bibtex":"@unpublished{boler_observability-informed_2022,\n\ttitle = {Observability-{Informed} {Measurement} {Validation} for {Visual}-{Inertial} {Navigation}},\n\turl = {https://etd.auburn.edu//handle/10415/8218},\n\tabstract = {This thesis presents a measurement validation method for visual-inertial navigation and an\nimplementation of a visual-inertial estimator which makes use of it. Many autonomous plat-\nforms, especially flying ones, rely on accurate and reliable state estimates from a visual-inertial\nestimator to maintain safe and controlled flight towards a goal. The measurement validation\nmethod presented in this thesis makes use of a geometric analysis of the landmark measuremnt\nmodel to enable early and reliable validation, safely integrating high-quality measurements\ninto the state estimation process. First, the IMU and camera sensors are detailed along with\nthe sensor processing techniques necessary to make use of them. Next, a detailed description\nof two standard visual-inertial estimation approaches is presented to develop necessary back-\nground knowledge. Following these descriptions, an analysis of the relationships between the\ngeometry of landmark observation and the accuracy and reliability of landmark estimates is per-\nformed, concluding with the proposal of a new validation method which delays measurement\nprocessing until the landmark is predicted to be observable. Lastly, a visual-inertial estimator is\ndeveloped which makes use of the proposed method and tested on the EUROC dataset, the most\ncommon visual-inertial dataset, against several state-of-the-art estimators. In this comparison,\nthe proposed estimator demonstrates competitive performace, reliably producing positioning\nerrors of less than 0.5 meters over flights up to 120 meters long. 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