Vision-Based Estimation of Small Body Rotational State during the Approach Phase

TL;DR

This work presents a vision-based framework for autonomously estimating a small body's rotational state during the approach phase. By tracking surface features, fitting projected circle conics, and backprojecting to recover candidate rotation-axis directions, the method yields the rotation axis and origin (center of rotation) without ground support. The approach leverages a known rotational period and a heuristic axis-selection strategy to disambiguate multiple candidates, and is validated with extensive synthetic data for Bennu and Itokawa, showing robust axis estimation (below $10^{\circ}$ error for $80\%$ of cases). The results demonstrate the feasibility of onboard, ground-agnostic small-body characterization, with implications for early localization and gravity-field estimation in autonomous missions.

Abstract

The heterogeneity of the small body population complicates the prediction of small body properties before the spacecraft's arrival. In the context of autonomous small body exploration, it is crucial to develop algorithms that estimate the small body characteristics before orbit insertion and close proximity operations. This paper develops a vision-based estimation of the small-body rotational state (i.e., the center of rotation and rotation axis direction) during the approach phase. In this mission phase, the spacecraft observes the rotating celestial body and tracks features in images. As feature tracks are the projection of the landmarks' circular movement, the possible rotation axes are computed. Then, the rotation axis solution is chosen among the possible candidates by exploiting feature motion and a heuristic approach. Finally, the center of rotation is estimated from the center of brightness. The algorithm is tested on more than 800 test cases with two different asteroids (i.e., Bennu and Itokawa), three different lighting conditions, and more than 100 different rotation axis orientations. Each test case is composed of about 250 synthetic images of the asteroid which are used to track features and determine the rotational state. Results show that the error between the true rotation axis and its estimation is below $10^{\circ}$ for $80\%$ of the considered test cases, implying that the proposed algorithm is a suitable method for autonomous small body characterization.

Figures (13)

• Figure 1: The observational geometry during small body approach.
• Figure 2: Example of the ellipse fitting from tracked features.
• Figure 3: The geometrical construction generating the two circles.
• Figure 5: The estimation of the circles from the two possible rotation axis candidates.
• Figure 6: The perpendicular-axis case reconstruction