Abstract
This paper investigates the efficacy of Convolutional Neural Network (CNN) based methods to navigate autonomously around asteroids. The main contribution of this work is the successful development of a first-of-a-kind pose estimation pipeline, consisting of a CNN-based feature detector and a Perspective-n-Points (PnP) solver to allow accurate, safe, and autonomous distance estimation with respect to a target asteroid. A top-down CNN-based feature detector is developed, consisting of an object and keypoint detection network in sequence, which detects n pre-defined keypoints, designated on the target's 3D model, within the 2D image. The simulated target asteroid is Bennu, a subkilometer asteroid with a spinning top-shape and pronounced equatorial bulge. The networks have been trained and evaluated on synthetic datasets created in this work, consisting of 32,352 images with a variety of poses from 4.5 to 9 km from the asteroid, for different illumination conditions, asteroid orientations, and image corruptions that emulate real sensor artifacts. The pipeline could achieve a mean and median line-of-sight distance estimate of around 42 m and 30 m, respectively, at a confidence level of 90% for the large relative range, while satisfying the accuracy requirement of a maximum of 10% knowledge error for 99.979% of the cases.
Original language | English |
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Title of host publication | Proceedings of the AIAA SCITECH 2025 Forum |
Number of pages | 28 |
ISBN (Electronic) | 978-1-62410-723-8 |
DOIs | |
Publication status | Published - 2025 |
Event | AIAA SCITECH 2025 Forum - Orlando, United States Duration: 6 Jan 2025 → 10 Jan 2025 |
Conference
Conference | AIAA SCITECH 2025 Forum |
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Country/Territory | United States |
City | Orlando |
Period | 6/01/25 → 10/01/25 |