18 citations · 19 across the 7 of their papers we have counts for
8 papers
DreamSat-Bench: Development and Initial Testing of a Testbed for AI-Based Pose Estimation from 3D Reconstruction
Alex Posadas-Nava, August Berne, Giovanni Lavezzi +8
This paper presents the development and initial testing of DreamSat-Bench, a modular rendezvous and proximity operation testbed designed to benchmark AI-based relative navigation t…
DreamSat-Pose: Spacecraft Pose Estimation from Single-View 3D Reconstructions and Learned 2D-3D Feature Matching
Josiane Uwumukiza, Jocelyn Zhao, Giovanni Lavezzi +5
6-DoF pose estimation is a critical task in autonomous rendezvous and proximity operations. In the case of an unknown target, this task becomes challenging as it shall be paired wi…
Advancing AI Challenges for the United States Department of the Air Force
Christian Prothmann, Vijay Gadepally, Jeremy Kepner +35
The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers…
DreamSat-2.0: Towards a General Single-View Asteroid 3D Reconstruction
Santiago Diaz, Xinghui Hu, Josiane Uwumukiza +3
To enhance asteroid exploration and autonomous spacecraft navigation, we introduce DreamSat-2.0, a pipeline that benchmarks three state-of-the-art 3D reconstruction models-Hunyuan-…
DreamSat: Towards a General 3D Model for Novel View Synthesis of Space Objects
Nidhi Mathihalli, Audrey Wei, Giovanni Lavezzi +4
Novel view synthesis (NVS) enables to generate new images of a scene or convert a set of 2D images into a comprehensive 3D model. In the context of Space Domain Awareness, since sp…
Parameters Evolution in Source-Sink Space Population Evolutionary Models
Erin Ashley, Carla Simon Sanz, Simone Servadio +1
MOCAT-SSEM is a Source-Sink model that predicts the Low Earth Orbit (LEO) space population divided into families using a predefined set of interaction parameters. Thanks to data fr…
Propagation of Uncertainty with the Koopman Operator
Simone Servadio, Giovanni Lavezzi, Christian Hofmann +2
This paper proposes a new method to propagate uncertainties undergoing nonlinear dynamics using the Koopman Operator (KO). Probability density functions are propagated directly usi…
Nonlinear Programming Solvers for Unconstrained and Constrained Optimization Problems: a Benchmark Analysis
Giovanni Lavezzi, Kidus Guye, Marco Ciarcià
In this paper we propose a set of guidelines to select a solver for the solution of nonlinear programming problems. With this in mind, we present a comparison of the convergence pe…