Deep Learning for Real-Time Satellite Pose Estimation on Tensor Processing Units

Lotti, Alessandro; Modenini, Dario; Tortora, Paolo; Saponara, M.; Perino, Maria Antonietta

doi:10.2514/1.a35496

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Research derived from the spacecraft pose estimation challenge based on datasets [20,21] show that the pose of a known non-cooperative spacecraft can be reliably estimated using a monocular camera [22][23][24][25]. However, these techniques fail for an unknown spacecraft.…”

Section: Computer Vision For On-orbit Operationsmentioning

confidence: 99%

Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting

Nguyen,

Sandidge,

Mahendrakar

et al. 2024

Aerospace

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The accelerating deployment of spacecraft in orbit has generated interest in on-orbit servicing (OOS), inspection of spacecraft, and active debris removal (ADR). Such missions require precise rendezvous and proximity operations in the vicinity of non-cooperative, possibly unknown, resident space objects. Safety concerns with manned missions and lag times with ground-based control necessitate complete autonomy. This requires robust characterization of the target’s geometry. In this article, we present an approach for mapping geometries of satellites on orbit based on 3D Gaussian splatting that can run on computing resources available on current spaceflight hardware. We demonstrate model training and 3D rendering performance on a hardware-in-the-loop satellite mock-up under several realistic lighting and motion conditions. Our model is shown to be capable of training on-board and rendering higher quality novel views of an unknown satellite nearly 2 orders of magnitude faster than previous NeRF-based algorithms. Such on-board capabilities are critical to enable downstream machine intelligence tasks necessary for autonomous guidance, navigation, and control tasks.

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