[{"content":"Role: Project Manager, University of Luxembourg\nLunarAssembly is a project funded by the Luxembourg National Research Fund (FNR) on teleoperated navigation and manipulation under large time delays for robotic assembly on the Moon. It brings together XR telepresence, 3D terrain reconstruction and hazard detection for rovers, in collaboration with TU Wien (Austria).\nOutputs Earth to Moon Bilateral Teleoperation for Lunar Assembly: A Unified Framework for Shared Autonomy, Skill Learning, and Telepresence, IEEE Conference on Telepresence, 2026 ","externalUrl":null,"permalink":"/projects/lunarassembly/","section":"Projects","summary":"Teleoperated navigation and manipulation for robotic lunar assembly. FNR project in collaboration with TU Wien.","title":"LunarAssembly","type":"projects"},{"content":" Role: Project Coordinator and proposal co-author, University of Luxembourg (2025 to 2026)\nAI4SPACE-XPLORE (Advanced AI Solutions for Space Exploration) is a study funded by the European Space Agency to build a strategic roadmap for the integration of artificial intelligence in space exploration activities. It responded to ESA Invitation to Tender 1-12786, Study for the Integration of AI in Space Exploration Activities.\nThe consortium was led by the University of Luxembourg in collaboration with DLR (Germany). The proposal was submitted in July 2025, awarded EUR 200k, and the study was completed in September 2026.\n","externalUrl":null,"permalink":"/projects/ai4space-xplore/","section":"Projects","summary":"ESA-funded study for a strategic roadmap on AI in space exploration. Project Coordinator, with DLR, 2025 to 2026.","title":"AI4SPACE-XPLORE","type":"projects"},{"content":"","externalUrl":"https://dpisanti.github.io/geo-loftr/","permalink":"","section":"Projects","summary":"Geometry-aided deep image matching for map-based localization of future Mars helicopters under challenging illumination. ICRA 2026.","title":"Geo-LoFTR","type":"projects"},{"content":"","externalUrl":"https://github.com/nasa-jpl/martian","permalink":"","section":"Projects","summary":"Open-source Blender framework that renders pose-annotated aerial imagery of Mars from HiRISE orbital data. Best Poster Award, ICRA 2026 workshop.","title":"MARTIAN","type":"projects"},{"content":" Role: Visiting Student Researcher, NASA Jet Propulsion Laboratory (Oct 2021 to Dec 2022)\nThe project The Lunar Crater Radio Telescope (LCRT) is a NASA JPL concept for a radio telescope on the far side of the Moon. A 350 m wire mesh reflector would be deployed inside a lunar crater, with a receiver suspended at its focus. On the far side, the Moon shields the telescope from radio interference, and the absence of Earth's ionosphere opens wavelengths longer than 10 m, which cannot be observed from the ground. The goal is to detect the faint 21-cm signal of neutral hydrogen from the Cosmic Dark Ages, the epoch before the first stars formed.\nMy contribution I developed the science pipeline for the concept within its NIAC Phase II study.\nScience forward model. I built the model that predicts what LCRT would observe, as the basis for assessing its science return. Signal extraction. I integrated a pattern recognition pipeline into a simulated lunar observation framework, retrieving the Dark Ages signal to within 20 mK over a one-year mission, despite galactic foregrounds several orders of magnitude brighter. Simulated datasets. I generated training and test sets of sky observations from the lunar far side with the full LCRT antenna model, including crater morphology and the dielectric properties of the regolith. This work received the NOVA Award from the Mobility and Robotic Systems Section of NASA JPL in 2024.\nOutputs Paper: Modelling Science Return from the Lunar Crater Radio Telescope on the Far Side of the Moon, Philosophical Transactions of the Royal Society A, 2024 Code and data: LCRT science model, JPL Open Repository Project page: Lunar Crater Radio Telescope on the Farside of the Moon, JPL Robotics ","externalUrl":null,"permalink":"/projects/lcrt/","section":"Projects","summary":"Science forward model and signal extraction for the Lunar Crater Radio Telescope, a radio telescope concept for the far side of the Moon. NASA JPL, 2021 to 2022.","title":"LCRT Science Pipeline","type":"projects"},{"content":" Download CV (PDF) Research experience University of Luxembourg, Space Robotics Group (SnT)Jun 2025 to present Postdoctoral Researcher, Luxembourg City Learning-based vision for map-relative navigation of aerial and landing platforms under challenging illumination and over unknown terrain. Project Manager of LunarAssembly (FNR, with TU Wien) and Project Coordinator of AI4SPACE-XPLORE (ESA, with DLR).\nNASA Jet Propulsion Laboratory, Mobility and Robotic Systems Section Visiting Student Researcher, LoRNA project, Pasadena, CAFeb 2023 to Dec 2024 Deep learning for map-relative localization of future Mars rotorcraft on long-range flights under challenging illumination. Conceived Geo-LoFTR, a geometry-aided deep image matcher, and MARTIAN, a framework that renders pose-annotated aerial imagery of Mars. More\nVisiting Student Researcher, LCRT project, Pasadena, CAOct 2021 to Dec 2022 Science forward model and signal extraction pipeline for the Lunar Crater Radio Telescope, a NIAC concept to detect the 21-cm signal of the Cosmic Dark Ages. More\nEuropean Space Agency, 72nd Parabolic Flight CampaignNov 2019 to Dec 2019 Payload Operator, Bordeaux, France Assembled and operated a payload from Université Libre de Bruxelles to test artificial droplet morphogenesis in low gravity, over three days of flight aboard the Airbus A310 ZERO-G.\nUniversité Libre de Bruxelles, Microgravity Research CentreMar 2019 to Sep 2019 Erasmus+ Trainee, Brussels, Belgium Research on graphene-enhanced loop heat pipes for space thermal management.\nEducation Scuola Superiore Meridionale, NaplesFeb 2025 Ph.D. in Cosmology, Space Science and Space Technology Doctoral research conducted at NASA Jet Propulsion Laboratory (2021 to 2024). Thesis: Vision-based Geo-Localization of Future Mars Rotorcraft in Challenging Illumination Conditions using Deep Learning.\nUniversity of Naples Federico IIOct 2019 M.S. in Aerospace Engineering and Astronautics, 110/110 magna cum laude University of Naples Federico IIMar 2017 B.S. in Aerospace Engineering, 110/110 cum laude Grants AI4SPACE-XPLORE: Advanced AI Solutions for Space Exploration2025 to 2026 Co-author and Project Coordinator; consortium led by the University of Luxembourg ESA Invitation to Tender 1-12786. Awarded EUR 200k; completed in September 2026.\nAwards Workshop Best Poster Award, ICRA 2026, Vienna. Awarded by the IEEE-RAS Technical Committee on Space Robotics for MARTIAN: A Rendering Framework for Aerial Mars Imagery from HiRISE Orbital Data. NOVA Award, NASA Jet Propulsion Laboratory, 2024. Notable Organizational Value Added award from the Mobility and Robotic Systems Section, for the science pipeline of the Lunar Crater Radio Telescope. AIAA Spacecraft Structures Best Paper Award, AIAA SciTech 2023, for Kilometer-Scale Parabolic Reflector for a Radio Telescope in a Lunar Crater. 1st place, Lab2Moon Global Challenge, Team Indus, Bangalore, 2017, for the lunar experiment concept Radio-Shield. 1st place, NASA Space Apps Challenge, Naples edition, 2016, for the Mars surface mobility concept SRS Explorer. Mentoring Christos Kyriazis, PhD advising, University of Luxembourg, 2025 to present. Teleoperated robotic navigation for lunar assembly and deep learning hazard segmentation. Lakshmikanth Nageswar, MSc thesis advising, IIT Kharagpur, 2025 to 2026. Robust state estimation in GPS-denied, perceptually degraded environments, fusing map-relative localization with visual-inertial odometry. Ariane Marongiou, MSc internship supervision, University of Luxembourg, 2026. Contribution to the ESA-funded AI4SPACE-XPLORE study. Peer review IEEE International Conference on Robotics and Automation (ICRA); International Journal of Robotics Research (IJRR); International Conference on Space Robotics (iSpaRo).\nSkills Programming: Python, C++, MATLAB/Simulink Machine learning: PyTorch, TensorFlow, ONNX; embedded deployment with TensorFlow Lite and the Qualcomm Neural Processing SDK Tools: Blender, OpenCV, Git, LaTeX Expertise: map-relative localization, image registration, multimodal deep learning, feature matching, camera pose estimation (PnP, RANSAC), synthetic data generation, image processing Certifications Space Resources Professional Course, ESRIC, Luxembourg, 2026 NASA Transform to Open Science (TOPS), Open Science 101, 2024 Licensed Professional Engineer, University of Naples Federico II, 2021 ","externalUrl":null,"permalink":"/cv/","section":"Dario Pisanti","summary":"","title":"Curriculum Vitae","type":"page"},{"content":" I am a Postdoctoral Researcher in the Space Robotics Group at SnT, University of Luxembourg. My research is at the intersection of computer vision and machine learning for planetary robotics, with a focus on map-relative navigation for aerial and landing platforms. These platforms must operate without GPS, over unknown terrain, under lighting that swings from harsh glare to deep shadow, and with limited onboard computing power. My broader goal is to give them the perception to navigate autonomously in such extreme environments, expanding what planetary robots can explore on their own, opening new frontiers for science, and reducing the risk of failure in critical mission operations.\nI carried out my doctoral research at NASA Jet Propulsion Laboratory as a Visiting Student Researcher, and received my Ph.D. from the Scuola Superiore Meridionale in Naples, Italy. On the LoRNA project I conceived Geo-LoFTR, a geometry-aided deep image matcher that fuses orbital imagery and elevation data to register onboard images to orbital maps under severe illumination changes. I also built MARTIAN, a framework that renders pose-annotated aerial imagery of Mars from HiRISE orbital data. Earlier, on the Lunar Crater Radio Telescope project, I developed the science forward model for a radio telescope designed to be deployed robotically on the far side of the Moon and to detect the faint 21-cm signal of neutral hydrogen from the Cosmic Dark Ages.\nResearch interests Map-relative localization for Mars rotorcraft Terrain relative navigation for autonomous landing Image registration in GNSS-denied environments Deep learning and deployment on embedded processors Hazard detection and 3D terrain reconstruction Selected publications Geometry-aided Vision-based Localization of Future Mars Helicopters in Challenging Illumination Conditions D. Pisanti, R. Hewitt, R. Brockers, G. Georgakis IEEE International Conference on Robotics and Automation (ICRA), 2026 paper/arXiv/project page/code/weights/dataset MARTIAN: A Rendering Framework for Aerial Mars Imagery from HiRISE Orbital Data D. Pisanti, G. Georgakis ICRA Workshop on Perceptual Challenges for Planetary Exploration, 2026Best Poster Award arXiv/code A Map-based Localization System for Ingenuity using Deep Image Matching G. Georgakis, D. Pisanti, N. Williams, C. Mauceri, G. Kubiak, A. Ansar, R. Brockers IEEE Transactions on Field Robotics (T-FR), 2025 paper Modelling Science Return from the Lunar Crater Radio Telescope on the Far Side of the Moon D. Pisanti, A. Goel, G. Gupta, M. Arya, B. Byron, …, S. Bandyopadhyay Philosophical Transactions of the Royal Society A, 2024 paper/code \u0026amp; data All publications\n","externalUrl":null,"permalink":"/","section":"Dario Pisanti","summary":"","title":"Dario Pisanti","type":"page"},{"content":"Projects I have led or developed, from robotic lunar assembly and AI strategy for space exploration to the navigation of future Mars rotorcraft and the science of a radio telescope on the far side of the Moon.\n","externalUrl":null,"permalink":"/projects/","section":"Projects","summary":"","title":"Projects","type":"projects"},{"content":" Vision-based navigation and robotics Earth to Moon Bilateral Teleoperation for Lunar Assembly: A Unified Framework for Shared Autonomy, Skill Learning, and Telepresence H. T. Dinc, J. Heidersberger, C. Kyriazis, T. Wagner, A. Frantz, D. Pisanti, F. Kong, M. A. Olivares-Mendez, D. Lee, C. Ott IEEE Conference on Telepresence, 2026 Geometry-aided Vision-based Localization of Future Mars Helicopters in Challenging Illumination Conditions D. Pisanti, R. Hewitt, R. Brockers, G. Georgakis IEEE International Conference on Robotics and Automation (ICRA), 2026 paper/arXiv/project page/code/weights/dataset MARTIAN: A Rendering Framework for Aerial Mars Imagery from HiRISE Orbital Data D. Pisanti, G. Georgakis ICRA Workshop on Perceptual Challenges for Planetary Exploration, 2026Best Poster Award arXiv/code A Map-based Localization System for Ingenuity using Deep Image Matching G. Georgakis, D. Pisanti, N. Williams, C. Mauceri, G. Kubiak, A. Ansar, R. Brockers IEEE Transactions on Field Robotics (T-FR), 2025 paper Design and Case Studies of CORTO, an Open Access Rendering Tool for Celestial and Artificial Bodies M. Pugliatti, C. Buonagura, D. Pisanti, N. Faraco, M. Maestrini, A. Pizzetti, F. Topputo International Astronautical Congress (IAC), 2024 paper Lunar radio astronomy Survey of Mission Concepts for Exploring the Dark Ages Universe S. Bandyopadhyay, A. Goel, G. Gupta, D. Pisanti, P. Goldsmith, T.-C. Chang, …, J. Lazio IEEE Aerospace Conference, 2025 paper Modelling Science Return from the Lunar Crater Radio Telescope on the Far Side of the Moon D. Pisanti, A. Goel, G. Gupta, M. Arya, B. Byron, …, S. Bandyopadhyay Philosophical Transactions of the Royal Society A, 2024 paper/code \u0026amp; data Reflector Antenna for Lunar Crater Radio Telescope G. Gupta, N. Chahat, A. Goel, M. Arya, D. Pisanti, P. Goldsmith, …, S. Bandyopadhyay IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, 2023 paper Lift Wire Deployment and Anchoring System for the Lunar Crater Radio Telescope on the Far Side of the Moon R. Wang, V. P. Gehlot, S. Bandyopadhyay, P. M. McGarey, B. Byron, D. Pisanti, …, K. Jenks AIAA SciTech Forum, 2023 paper Kilometer-Scale Parabolic Reflector for a Radio Telescope in a Lunar Crater M. Arya, J. T. Herrscher, D. Pisanti, A. Verniani, M. Delapierre, …, S. Bandyopadhyay AIAA SciTech Forum, 2023AIAA Spacecraft Structures Best Paper Award paper Lunar Crater Radio Telescope (LCRT) on the Far Side of the Moon S. Bandyopadhyay, A. Goel, D. Pisanti, J. Lazio, P. Goldsmith, …, V. Vustyansky NASA Innovative Advanced Concepts (NIAC) Phase II Final Report, 2023 pdf ","externalUrl":null,"permalink":"/publications/","section":"Dario Pisanti","summary":"","title":"Publications","type":"page"},{"content":" MbL-Mars Map-based localization pipeline for Mars rotorcraft, with trained Geo-LoFTR models. NASA-JPL GitHub, 2026.\nCode / Weights / Project page\nMARTIAN Blender-based framework for rendering pose-annotated aerial Mars imagery from HiRISE orbital data. NASA-JPL GitHub, 2026.\nCode / Paper\nGeo-LoFTR Dataset Synthetic Mars training and test data rendered with MARTIAN, including orthographic maps, navigation queries and training pairs under varied illumination. JPL Open Repository, 2026.\nData\nLCRT Science Model Radio sky simulations and code for the science forward model of the Lunar Crater Radio Telescope. JPL Open Repository, 2023.\nCode and data\n","externalUrl":null,"permalink":"/software/","section":"Dario Pisanti","summary":"","title":"Software \u0026 Data","type":"page"},{"content":"","externalUrl":null,"permalink":"/tags/","section":"Tags","summary":"","title":"Tags","type":"tags"}]