Vision-Based, Terrain-Aided Navigation with Decentralized Fusion and Finite Set Statistics


Terrain-related information, in the form of features extracted from images, presents a rich data source that can be harvested to facilitate drastic improvements in navigation when conventional data sources, such as GPS, are not available. Conventional implementation of such data types requires image correlation techniques that interrupt streamlined transmission of statistics through a navigation filter, oftentimes leading to time-wise correlations that are erroneously ignored. This paper proposes leveraging finite set statistics to recast the terrain feature data into a simultaneous localization and mapping problem. Decentralized data fusion is employed to augment a standard extended Kalman filter-based navigation with the terrain data. Theoretical results are supported with a simulated descent to landing navigation scenario that demonstrates the improvements offered by augmenting standard navigation with terrain aiding.


Mechanical and Aerospace Engineering

Keywords and Phrases

Data fusion; Image enhancement; Kalman filters; Landforms; Set theory, Data-sources; Decentralized data fusion; Finite set statistics; Image correlation techniques; Navigation filters; Simultaneous localization and mapping problems; Terrain aided navigation; Terrain features, Navigation

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Article - Journal

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© 2019 Wiley-Blackwell, All rights reserved.

Publication Date

01 Aug 2019