Unidentified landmark navigation.

Bellantoni, Juan F.

doi:10.2514/3.4222

Cited by 14 publications

(3 citation statements)

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“…One is an implicit bias method without estimating landmark's position. Such method combines two or more observations at different times into a single one that does not depend upon the landmark's position [2], [3] and spacecraft's orbital information is estimated using a kalman filter by delay-state augmentation [4]. Another approach is augmenting landmark's position with the spacecraft's position and velocity vector to form a new state vector and then a Kalman filter is used to estimate spacecraft's state as well as the landmark's position [5].…”

Section: Introductionmentioning

confidence: 99%

Constrained estimation for autonomous navigation using unknown landmarks

Li¹,

Wang²,

Huang³

et al. 2013

2013 Chinese Automation Congress

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This paper presents a new method for autonomous navigation using unknown landmarks on the surface of an ellipsoidal planet, and geometry constraints of landmarks' position that satisfy an ellipsoid equation are considered. Both of and unconstrained and constrained estimation are developed. An implicit bias method by augmenting the current state with a delay state to eliminate unknown landmarks positions in the measurement equation is used to form an unconstrained estimation of spacecraft's state. Once the unconstrained estimation is derived, geometry constraints of landmarks' position are transformed to constraints of spacecraft's state. The unconstrained estimation is then projected onto the constrained space to form a constrained estimation. Numerical simulations are also provided to assess the performance of the proposed new method.

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Section: Introductionmentioning

confidence: 99%

Constrained estimation for autonomous navigation using unknown landmarks

Li¹,

Wang²,

Huang³

et al. 2013

2013 Chinese Automation Congress

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“…This has led to the continued use of horizon-based OPNAV at close distances where it would otherwise be better to use surface features. It has also led to navigation with unknown landmarks [8,23,80,137] or visual odometry [27]. Unfortunately, horizon-based OPNAV is not always practical (especially at close ranges) and unknown landmarks do not generally provide full state observability.…”

Section: Introductionmentioning

confidence: 99%

Lunar Crater Identification in Digital Images

2021

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It is often necessary to identify a pattern of observed craters in a single image of the lunar surface and without any prior knowledge of the camera’s location. This so-called “lost-in-space” crater identification problem is common in both crater-based terrain relative navigation (TRN) and in automatic registration of scientific imagery. Past work on crater identification has largely been based on heuristic schemes, with poor performance outside of a narrowly defined operating regime (e.g., nadir pointing images, small search areas). This work provides the first mathematically rigorous treatment of the general crater identification problem. It is shown when it is (and when it is not) possible to recognize a pattern of elliptical crater rims in an image formed by perspective projection. For the cases when it is possible to recognize a pattern, descriptors are developed using invariant theory that provably capture all of the viewpoint invariant information. These descriptors may be pre-computed for known crater patterns and placed in a searchable index for fast recognition. New techniques are also developed for computing pose from crater rim observations and for evaluating crater rim correspondences. These techniques are demonstrated on both synthetic and real images.

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“…This has led to the continued use of horizon-based OPNAV at close distances where it would otherwise be better to use surface features. It has also led to navigation with unknown landmarks [76,132,8,22,25]. Unfortunately, horizon-based OPNAV is not always practical (especially at close ranges) and unknown landmarks do not generally provide full state observability.…”

Section: Introductionmentioning

confidence: 99%

Lunar Crater Identification in Digital Images

Christian,

Derksen,

Watkins

2020

Preprint

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It is often necessary to identify a pattern of observed craters in a single image of the lunar surface and without any prior knowledge of the camera's location. This so-called "lost-in-space" crater identification problem is common in both crater-based terrain relative navigation (TRN) and in automatic registration of scientific imagery. Past work on crater identification has largely been based on heuristic schemes, with poor performance outside of a narrowly defined operating regime (e.g., nadir pointing images, small search areas). This work provides the first mathematically rigorous treatment of the general crater identification problem. It is shown when it is (and when it is not) possible to recognize a pattern of elliptical crater rims in an image formed by perspective projection. For the cases when it is possible to recognize a pattern, descriptors are developed using invariant theory that provably capture all of the viewpoint invariant information. These descriptors may be pre-computed for known crater patterns and placed in a searchable index for fast recognition. New techniques are also developed for computing pose from crater rim observations and for evaluating crater rim correspondences. These techniques are demonstrated on both synthetic and real images.

show abstract

Unidentified landmark navigation.

Cited by 14 publications

References 1 publication

Constrained estimation for autonomous navigation using unknown landmarks

Constrained estimation for autonomous navigation using unknown landmarks

Lunar Crater Identification in Digital Images

Lunar Crater Identification in Digital Images

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