Robust, Terrain-Aided Landing Navigation through Decentralized Fusion and Random Finite Sets


This paper explores the use of simultaneous localization and mapping with random finites sets to process terrain camera data and augment traditional Kalman filter-based navigation tools. Enabled by new theoretical developments, the presented methods employ both consider parameters and square-root factors of covariance in the interest of producing a practical, numerically stable navigation algorithm. Additionally, a model is presented for a data-driven learning procedure that permits observations of previously unexplored terrain to be instantiated into the filter and improve subsequent navigation estimates. The proposed techniques are evaluated in a simulated lunar descent scenario, and a Monte Carlo analysis confirms the statistical consistency of the described methods.

Meeting Name

AIAA Guidance, Navigation, and Control Conference, 2018 (2018: Jan. 8-12, Kissimmee, FL)


Mechanical and Aerospace Engineering

Keywords and Phrases

Aviation; Kalman filters; Landforms; Monte Carlo methods; Program processors, Decentralized fusions; Learning procedures; Monte carlo analysis; Navigation algorithms; Simultaneous localization and mapping; Statistical consistencies; Theoretical development; Traditional Kalman filters, Navigation

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2018 American Institute of Aeronautics and Astronautics (AIAA), All rights reserved.

Publication Date

01 Jan 2018