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Conway, Dylan T.

Publications and source records attributed to Conway, Dylan T..

3 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Performance Analysis of Terrain Relative Navigation Using Blue Origin New Shepard Suborbital Flight Telemetry

As part of a NASA Tipping Point Partnership with Blue Origin to mature precision lunar landing technologies, two test flights of the Blue Origin New Shepard vehicle carrying a NASA-developed sensor suite were conducted on 10/13/2020 and 08/26/2021 at the West Texas Launch Site (LS-1). Part of the acquired datasets, comprising data from an inertial measurement unit and a downward facing camera, was postprocessed through a JPL-developed prototype Visual Odometry and Map Relative Localization software (TRNVOSIM), and compared against ground truth acquired by the host vehicle navigation system. In this paper, we provide a description of the algorithms, the test setup, and the processed results.

Pedrotty, Samuel M.↗
NASA NTRS2017

A Minimal State Augmentation Algorithm for Vision-Based Navigation without Using Mapped Landmarks

This paper describes MAVeN (Minimal State Augmentation Algorithm for Vision-Based Navigation), which is a new algorithm for vision-based navigation that has only 21 states, yet is able to track features in successive camera images and use them to propagate estimates of the spacecraft position and velocity. The filter dimension drops to 12 if attitude information is already available. The low filter dimension makes MAVeN a very reliable and practical algorithm for real-time flight implementation. The main idea is to project observed features onto a rough shape model of the ground surface, which are then used by the filter as pseudo-landmarks. The shape model is assumed to be known beforehand, as would be obtained from prior surveillance of the landing site from orbit. MAVeN does not require pre-mapped landmarks, so it is able to navigate terrain that has not been previously observed up close. This property is especially important for close proximity operations in small body missions where ground surface features are being seen for the first time at close range. MAVeN is also able to hover motionless above the ground without position error growth, which is unusual for this class of vision-based navigation algorithms.

San Martin, A. Miguel↗