arXiv subjects

Dario Pisanti

Publications and source records attributed to Dario Pisanti.

3 recordsLinked to original sources

arXiv2026

MARTIAN: A Rendering Framework for Aerial Mars Imagery from HiRISE Orbital Data

Aerial navigation on Mars requires vision-based pipelines that are robust to the diverse illumination conditions and terrain morphology of the Martian surface. A key bottleneck for training and evaluating such methods is the scarcity of large-scale, annotated aerial datasets. We present MARTIAN, an open-source Blender-based rendering framework that leverages real HiRISE orbital map products to synthesize realistic aerial views of the Martian terrain under controllable lighting conditions and at varying altitudes. MARTIAN generates observations with accurate pose annotations, directly addressing the scarcity of training data for vision-based navigation on Mars. The framework has been validated through its deployment in concurrent work on map-based localization systems for Ingenuity and future Mars rotorcraft, where synthetically trained deep image matchers were successfully evaluated on real Mars imagery. MARTIAN is publicly available at: https://github.com/nasa-jpl/martian.

cs.CV↗

arXiv2026

Geometry-aided Vision-based Localization of Future Mars Helicopters in Challenging Illumination Conditions

Planetary exploration using aerial assets has the potential for unprecedented scientific discoveries on Mars. While NASA's Mars helicopter Ingenuity proved flight in Martian atmosphere is possible, future Mars rotorcraft will require advanced navigation capabilities for long-range flights. One such critical capability is Map-based Localization (MbL) which registers an onboard image to a reference map during flight to mitigate cumulative drift from visual odometry. However, significant illumination differences between rotorcraft observations and a reference map prove challenging for traditional MbL systems, restricting the operational window of the vehicle. In this work, we investigate a new MbL system and propose Geo-LoFTR, a geometry-aided deep learning model for image registration that is more robust under large illumination differences than prior models. The system is supported by a custom simulation framework that uses real orbital maps to produce large amounts of realistic images of the Martian terrain. Comprehensive evaluations show that our proposed system outperforms prior MbL efforts in terms of localization accuracy under significant lighting and scale variations. Furthermore, we demonstrate the validity of our approach across a simulated Martian day and on real Mars imagery. Code and datasets are available at: https://dpisanti.github.io/geo-loftr/.

cs.CV↗

arXiv2022

Probing the Cosmic Dark Ages with the Lunar Crater Radio Telescope

The Cosmic Dark Ages represent the period in the early evolution of the Universe, starting immediately after the decoupling of CMB photons from matter, and ending with the formation of the first stars and galaxies. The HI signal from the neutral hydrogen atoms is the only mechanism for us to understand this crucial phase in the cosmological history of the Universe and answer fundamental questions about the validity of the standard cosmological model, dark matter physics, and inflation. Due to cosmological redshift, this signal is now only observable in the 3-30~MHz frequency band, which is blocked from reaching the surface of the Earth by the ionosphere. In this paper, we present the design of the Lunar Crater Radio Telescope that intends to carry out unprecedented measurements of this signal by deploying a kilometer-sized parabolic reflector mesh inside a lunar crater on the far side of the Moon and suspending a receiver at its focus.

astro-ph.CO↗