Graph-based terrain relative navigation with optimal landmark database selection
Abstract
Terrain relative navigation (TRN) offers a means to constrain absolute vehicle position and attitude without using a GPS receiver or star camera, such as for unmanned aerial vehicles or planetary landing spacecraft, using a pre-computed terrain database of distinctive landmarks in the operational environment. However, depending on the length of the planned trajectory, these terrain landmark databases may grow prohibitively large for the onboard data storage, processing, and communication capabilities of these types of vehicles. In this paper, we introduce a definition of landmark utility - which encapsulates the observation probability of a landmark and its spatial distribution relative to nearby landmarks in the database - as a measure of the value of potential line-of sight measurements to that landmark. We additionally present an efficient algorithm to sort all potential landmarks in an environment based on their relative utility without access to the actual sensor measurements or trajectory. This enables pre-determination of a limited-size terrain landmark database containing the N-best landmarks in the environment, which is applicable to any landmark-based TRN system. To evaluate our landmark selection algorithm, we additionally introduce an incremental smoothing-based approach to TRN using a Bayesian factor graph representation. This system is capable of overcoming several challenges associated with TRN systems, including relinearization of past measurements, simultaneously utilizing pre-mapped and opportunistic features in a common estimator, and efficient sensor fusion in a common, probabilistic framework. We provide results showing the benefits of our utility-based landmark selection compared to alternative selection approaches, using evaluation metrics independent of any particular TRN estimation framework and the navigation performance of our graph-based TRN system.
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Landmark selection algorithms have been somewhat helpful in diminishing these undesirable effects.
“Landmark selection algorithms have been somewhat helpful in diminishing these undesirable effects16.”
From Probabilistic regression for autonomous terrain relative navigation via multi-modal feature learning (2024) · cites this paper as [16]
A higher number of detected features corresponds to lower position estimate uncertainty in terrain relative navigation.
“A higher number of detected features corresponds to lower position estimate uncertainty in terrain relative navigation, as evinced by [21].”
From Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection (2020) · cites this paper as [21]
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- Authors
- Steiner, T. J., Brady, T. M., & Hoffman, J. A.
- Affiliations
- Massachusetts Institute of Technology (US)
- Draper Laboratory (US)
- ORCID
- Hoffman, Jeffrey A.
- Venue
- 2015 IEEE Aerospace Conference
- Publisher
- IEEE
- Published
- 2015-03
- Pages
- 1-12
- ISSN
- 1095-323X (ISSN-L)
- OpenAlex
- W1571337729
- Cited by
- 16
- References
- 40
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 3 · 2022: 2 · 2021: 1 · 2020: 3 · 2019: 1 · 2017: 4
- FWCI
- 2.31
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Aerospace Conference (AERO) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › 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
- terrain relative navigation, landmark selection, graph-based navigation, relinearization, sensor fusion, unmanned aerial vehicles, GPS-denied navigation
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