# Keypoint-based Stereophotoclinometry for Characterizing and Navigating Small Bodies: A Factor Graph Approach

arXiv: https://arxiv.org/abs/2312.06865 · HTML: https://mlsanity.com/paper/2312.06865/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 78 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [DKM: Dense Kernelized Feature Matching for Geometry Estimation](https://mlsanity.com/paper/2202.00667/) (2022)
- [Graph-Cut RANSAC](https://mlsanity.com/paper/1706.00984/) (2017)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images](https://mlsanity.com/paper/2003.12642/) (2020)
- [PS-FCN: A Flexible Learning Framework for Photometric Stereo](https://mlsanity.com/paper/1807.08696/) (2018)
- [A Differential Volumetric Approach to Multi-View Photometric Stereo](https://mlsanity.com/paper/1811.01984/) (2018)
- [Multi-View Photometric Stereo Revisited](https://mlsanity.com/paper/2210.07670/) (2022)
- [AstroVision: Towards Autonomous Feature Detection and Description for Missions to Small Bodies Using Deep Learning](https://mlsanity.com/paper/2208.02053/) (2022)
- [Deep Learning Methods for Calibrated Photometric Stereo and Beyond](https://mlsanity.com/paper/2212.08414/) (2022)
- [Distributed Global Structure-from-Motion with a Deep Front-End](https://mlsanity.com/paper/2311.18801/) (2023)
- [AstroSLAM: Autonomous Monocular Navigation in the Vicinity of a Celestial Small Body -- Theory and Experiments](https://mlsanity.com/paper/2212.00350/) (2022)

## Built on by (most cited first, 1 of 1)

- [Stereophotoclinometry Revisited](https://mlsanity.com/paper/2504.08252/) (2025)

## Abstract (arXiv)

This paper proposes the incorporation of techniques from stereophotoclinometry (SPC) into a keypoint-based structure-from-motion (SfM) system to estimate the surface normal and albedo at detected landmarks to improve autonomous surface and shape characterization of small celestial bodies from in-situ imagery. In contrast to the current state-of-the-practice method for small body shape reconstruction, i.e., SPC, which relies on human-in-the-loop verification and high-fidelity a priori information to achieve accurate results, we forego the expensive maplet estimation step and instead leverage dense keypoint measurements and correspondences from an autonomous keypoint detection and matching method based on deep learning to provide the necessary photogrammetric constraints. Moreover, we develop a factor graph-based approach allowing for simultaneous optimization of the spacecraft's pose, landmark positions, Sun-relative direction, and surface normals and albedos via fusion of Sun sensor measurements and image keypoint measurements. The proposed framework is validated on real imagery of the Cornelia crater on Asteroid 4 Vesta, along with pose estimation and mapping comparison against an SPC reconstruction, where we demonstrate precise alignment to the SPC solution without relying on any a priori camera pose and topography information or humans-in-the-loop