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Cited by 37Spacecraft Dynamics and ControlInertial Sensor and NavigationRobotics and Sensor-Based Localization

On Lunar On-Orbit Vision-Based Navigation: Terrain Mapping, Feature Tracking Driven EKF

Singh, Leena, Lim, Sungyung·2008·AIAA Guidance, Navigation and Control Conference and Exhibit
DOI 10.2514/6.2008-6834
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Abstract

This paper presents an approach to vision guided, feature-based navigation that integrates absolute navigation concepts involving image feature extraction and matching to a database, with relative navigation including feature flow and tracking. The approach uses an extended Kalman filter (EKF) for vehicle navigation to integrate the two methodologies and thus capture the advantages of both techniques. This methodology was specifically developed for navigation in lunar orbiting or landing applications. Crater fiducial markers from the lunar surface are identified in an image and associated to a database; these are tracked across the image plane as the lunar lander moves in its orbit. In addition to the vision sensor, an altimeter is used to supply altitude measurements above the surface of the moon. The EKF presented here estimates the inertial vehicle position and velocity states by tracking the fiducials, whose inertial coordinates are now known, across the image plane. The accuracy improvements of this approach are substantial compared to either the absolute navigation pose estimation methods or the relative navigation optical flow methods when used alone in the same scenarios. The dominant computational overhead in any of these methods is in acquiring and processing the images for the features. Therefore, there is little additional processing load associated with this method that either of the other vision-based techniques do not require. Images used in this research to develop and analyze the algorithms were obtained from NASA’s Clementine lunar mapping mission.

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Crossref · 10.2514/6.2008-6834

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13 of 13 references verified0 citation errors · 0 not checkable

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  • Craters database matching is typically exploited to retrieve the absolute pseudo-measurement.

    “In particular, literature demonstrates that craters database matching is typically exploited to retrieve the absolute pseudo-measurement [10,11].”

    From Optical navigation for Lunar landing based on Convolutional Neural Network crater detector (2022) · cites this paper as [10]

  • In prior work, database matching is performed via a nearest neighbor approach, linking the output from the object detector to the navigation filter.

    “In these papers, the database matching is performed via nearest neighbor approach, linking the output from the object detector to the navigation filter.”

    From Optical navigation for Lunar landing based on Convolutional Neural Network crater detector (2022) · cites this paper as [10]

  • The achieved inference time makes the ODN a promising, and fast, alternative to the iterative, and slower, processes of template matching and thresholding required in classical techniques.

    “the achieved inference time makes the ODN a promising, and fast, alternative to the iterative, and slower, processes of template matching and thresholding required in classical techniques [10].”

    From Optical navigation for Lunar landing based on Convolutional Neural Network crater detector (2022) · cites this paper as [10]

  • [12] incorporated random sample consensus (RANSAC) to check whether the translation vectors between detected craters and known craters were consistent in images where multiple craters were detected; if a crater match produced a translation vector that was an outlier, then that match was rejected.

    “This approach incorporated random sample consensus (RANSAC) [13] to check whether the translation vectors between detected craters and known craters were consistent in images where multiple craters were detected.If a crater match produced a translation vector that was an outlier from the other crater matches, then that match was rejected.”

    From Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection (2020) · cites this paper as [12]

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Related

Authors
Singh, L., & Lim, S.
Affiliations
Draper Laboratory (US)
ORCID
Singh, Leena
Venue
AIAA Guidance, Navigation and Control Conference and Exhibit
Publisher
American Institute of Aeronautics and Astronautics
Published
2008-06-15
DOI
10.2514/6.2008-6834
OpenAlex
W2320879449
Cited by
37
References
13
Citations by year
2026: 1 · 2025: 2 · 2024: 4 · 2023: 7 · 2022: 4 · 2021: 3 · 2020: 5 · 2019: 2 · 2018: 1 · 2017: 1 · 2016: 2 · 2015: 2 · 2014: 1
FWCI
1.30
Type
conference paper
Language
en
Open access
closed
Locations
AIAA Guidance, Navigation, and Control Conference and Exhibit (publishedVersion)
Funders
Else Kröner-Fresenius-Stiftung
Topics
  • Physical Sciences › Engineering › Aerospace Engineering › Spacecraft Dynamics and Control
  • Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
  • Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
Keywords
visual navigation, feature tracking, extended Kalman filter, relative navigation, optical flow, lunar lander, altimeter, Clementine mission

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