VINSat: Solving the Lost-in-Space Problem with Visual-Inertial Navigation

Abstract

Rapid growth in the number of nanosatellite deployments has heightened the need for rapid, cost-effective, and accurate orbit determination (OD). This paper introduces a solution to this "lost-in-space" problem that we call Visual-Inertial Navigation for Satellites (VINSat). VINSat performs OD using data from an inertial measurement unit (IMU) and a low-cost RGB camera. Machine learning techniques are used to identify known landmarks in images captured by the spacecraft. These landmark locations are then combined with IMU data and a dynamics model in a batch nonlinear least-squares state estimator to determine the full state of the spacecraft. We validate VINSat in simulation using real nadir-pointing imagery and find that 85% of simulated satellites are localized to under 5 km within 6 hours (4 orbits). This performance substantially surpasses that of ground radar, demonstrating significantly faster and more precise localization without any reliance on ground infrastructure.