AstroSLAM: Autonomous monocular navigation in the vicinity of a celestial small body—Theory and experiments

Abstract

We propose AstroSLAM, a standalone vision-based solution for autonomous online navigation around an unknown celestial target small body. AstroSLAM is predicated on the formulation of the SLAM problem as an incrementally growing factor graph, facilitated by the use of the GTSAM library and the iSAM2 engine. By combining sensor fusion with orbital motion priors, we achieve improved performance over a baseline SLAM solution and outperform state-of-the-art methods predicated on pre-integrated inertial measurement unit factors. We incorporate orbital motion constraints into the factor graph by devising a novel relative dynamics—RelDyn—factor, which links the relative pose of the spacecraft to the problem of predicting trajectories stemming from the motion of the spacecraft in the vicinity of the small body. We demonstrate AstroSLAM’s performance and compare against the state-of-the-art methods using both real legacy mission imagery and trajectory data courtesy of NASA’s Planetary Data System, as well as real in-lab imagery data produced on a 3 degree-of-freedom spacecraft simulator test-bed.