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

arXiv: https://arxiv.org/abs/2212.00350 · HTML: https://mlsanity.com/paper/2212.00350/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 88 references that are in the map.
- First posted on arXiv 1 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [Batch Nonlinear Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression](https://mlsanity.com/paper/1412.0630/) (2014)
- [A Factor-Graph Approach for Optimization Problems with Dynamics Constraints](https://mlsanity.com/paper/2011.06194/) (2020)
- [Incremental Sparse GP Regression for Continuous-time Trajectory Estimation & Mapping](https://mlsanity.com/paper/1504.02696/) (2015)

## Built on by (most cited first, 7 of 7)

- [Vision-Based Estimation of Small Body Rotational State during the Approach Phase](https://mlsanity.com/paper/2302.11364/) (2023)
- [Keypoint-based Stereophotoclinometry for Characterizing and Navigating Small Bodies: A Factor Graph Approach](https://mlsanity.com/paper/2312.06865/) (2023)
- [Initialization of Monocular Visual Navigation for Autonomous Agents Using Modified Structure from Small Motion](https://mlsanity.com/paper/2409.16465/) (2024)
- [Stereophotoclinometry Revisited](https://mlsanity.com/paper/2504.08252/) (2025)
- [Efficient Feature Description for Small Body Relative Navigation using Binary Convolutional Neural Networks](https://mlsanity.com/paper/2304.04985/) (2023)
- [Navigation Around Unknown Space Objects Using Visible-Thermal Image Fusion](https://mlsanity.com/paper/2512.12203/) (2025)
- [Principal Component Analysis for Lunar Crater Detection](https://mlsanity.com/paper/2605.17125/) (2026)

## Abstract (arXiv)

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.