AstroSLAM - A Robust and Reliable Visual Localization and Pose Estimation Architecture for Space Robots in Orbit

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Space robotics is essential for all current and future space exploration and utilization missions. Advanced space-robotic technologies will enable in-orbit servicing and refueling of satellites, and will support more elaborate missions such as in-orbit large flexible structure assembly, debris removal, inspection, hardware upgrades, etc. For instance, current communication satellites have a typical lifetime of 10 to 15 years, at which point an otherwise perfectly functioning satellite runs out of propellant and is decommissioned. Refueling the satellite can add several years of additional lifetime and revenue. Future envisioned missions to the Moon, Mars and beyond will also require advanced robotic capabilities in orbit. Many future exploration missions envision integrated teams of astronauts and free flying “co-robots” that support or monitor the human crew activities. What is currently missing from these missions is the ability to provide full 4D (space-time) situational awareness using autonomous on-board perception and planning capabilities. Recent technological breakthroughs for ground robots pave the way for similar advancements in robotic in-orbit operations to enable routine robotic operations in space in the not-so-distant future. These include perception and planning algorithms, machine learning based pattern recognition, autonomy, new computer hardware architectures, human-machine interfaces, and dexterous manipulation, among many others. The outcome of the research will be the ability of astronauts and space robots to work together to enable inspection, monitoring, and classification of resident space objects; maneuvering and proximity operations and docking; salvage and retrieval of malfunctioning or tumbling spacecraft; and servicing, construction, repair, upgrade, and refueling missions of assets in orbit.This project will develop novel visual perception, localization, mapping, and planning algorithms that will enable new capabilities in terms of situational awareness for space robots that can work alone or alongside astronauts in orbit. The research plan includes the development of novel automated feature extraction and matching algorithms using deep neural network architectures, adapted to the challenging imaging conditions (collimated light, high contrast, lack of atmospheric scattering, paucity of distinctive features, etc.) and orbital motion constraints imposed in space. This will enable robust and reliable relative pose estimation, 3D shape reconstruction and characterization of space objects. Novel optimal planning and prediction methods based on a factor-graph optimization framework will be matched to these new perception capabilities to account for fuel usage and orbital motion constraints. The experimental validation of the theory will take place at the Georgia Tech Autonomous Spacecraft Testing of Robotic Operations in Space platform, a state-of-the-art spacecraft simulation platform. The research will involve both graduate and undergraduate students. The results of this research will be disseminated to the community by journal and conference publications, organization of invited workshops and seminar presentations, and by targeted exposure (press releases, interviews) to popular media.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.