AstroSLAM: Autonomous Monocular Navigation in the Vicinity of a Celestial Small Body -- Theory and Experiments

TL;DR

AstroSLAM is a vision-based SLAM system designed for autonomous navigation around small celestial bodies, integrating orbital motion priors and a novel relative dynamics factor to improve accuracy, validated through real and simulated data.

Contribution

The paper introduces AstroSLAM, a novel SLAM framework that incorporates orbital motion constraints via a new relative dynamics factor, enhancing navigation accuracy near small celestial bodies.

Findings

AstroSLAM outperforms baseline SLAM solutions in accuracy.

Effective integration of orbital motion priors improves navigation.

Validated on real mission imagery and simulated data.

Abstract

We propose AstroSLAM, a standalone vision-based solution for autonomous online navigation around an unknown target small celestial 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. We incorporate orbital motion constraints into the factor graph by devising a novel relative dynamics 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 the excellent performance of AstroSLAM using both real legacy mission imagery and trajectory data courtesy of NASA's Planetary Data System, as well as real in-lab imagery data generated on a 3 degree-of-freedom spacecraft simulator test-bed.