Online Inertia Tensor Identification for Non-Cooperative Spacecraft via Augmented UKF

Batu Candan; Simone Servadio

cs.ROMar 28, 2026

Online Inertia Tensor Identification for Non-Cooperative Spacecraft via Augmented UKF

Authors: Batu Candan, Simone Servadio

Organizations: Iowa State University, Ames, Iowa, USA, 50011.

Abstract

Autonomous proximity operations, such as active debris removal and on-orbit servicing, require high-fidelity relative navigation solutions that remain robust in the presence of parametric uncertainty. Standard estimation frameworks typically assume that the target spacecraft's mass properties are known a priori; however, for non-cooperative or tumbling targets, these parameters are often unknown or uncertain, leading to rapid divergence in model-based propagators. This paper presents an augmented Unscented Kalman Filter (UKF) framework designed to jointly estimate the relative 6-DOF pose and the full inertia tensor of a non-cooperative target spacecraft. The proposed architecture fuses visual measurements from monocular vision-based Convolutional Neural Networks (CNN) with depth information from LiDAR to constrain the coupled rigid-body dynamics. By augmenting the state vector to include the six independent elements of the inertia tensor, the filter dynamically recovers the target's normalized mass distribution in real-time without requiring ground-based pre-calibration. To ensure numerical stability and physical consistency during the estimation of constant parameters, the filter employs an adaptive process noise formulation that prevents covariance collapse while allowing for the gradual convergence of the inertial parameters. Numerical validation is performed via Monte Carlo simulations, demonstrating that the proposed Augmented UKF enables the simultaneous convergence of kinematic states and inertial parameters, thereby facilitating accurate long-term trajectory prediction and robust guidance in non-cooperative deep-space environments.

Figures & tables

Parameter	Value
$FoV$ (Field of view)	45 $\degree$
$f_{x}$ (Focal length in x-direction)	1920 (pxs)
$f_{y}$ (Focal length in y-direction)	1280 (pxs)
$c_{x}$ (Principal point x-coordinate)	960 (pxs)
$c_{y}$ (Principal point y-coordinate)	640 (pxs)
Affine translation (std. dev.)	3 pixels (in both $x$ and $y$ )

Table 1: Camera intrinsic properties and simulation parameters

Figure 1: Monte Carlo convergence of the trace-normalized inertia states ( $N=100$ )

Figure 2: Monte Carlo convergence ( $N=100$ ) under uninformative isotropic initialization

Spacecraft Pose Estimation

Spacecraft pose estimation recovers a spacecraft’s position and orientation from images and other sensor data for navigation, rendezvous, and servicing.

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Monocular Navigation Relative to Unknown Spacecraft Using a Transformer-Aided Kalman Filter

Pol Francesch Huc, Simone D'Amico

Department of Aeronautics and Astronautics Stanford University Stanford, CA 94305

Kalman Filtering Relative Pose Estimation Spacecraft Rendezvous and Proximity Operations Visual Odometry Spacecraft Pose Estimation

Open paper AskPDF arXiv

This work presents a novel learning-based pipeline for pose estimation of unknown spacecraft using only monocular images from a single servicer. The approach combines a transformer-based neural network with a Multi-State Constraint Kalman Filter (MSCKF) to estimate the pose (i.e., position and orientation of the target spacecraft relative to the camera) throughout rendezvous and proximity operations. Unlike existing vision-based methods that require prior knowledge of the target shape or inertia properties, rely on additional sensing modalities such as depth, lidar, or stereo, or only recover translation up to scale, the proposed pipeline generalizes to previously unseen spacecraft using a single monocular camera. The transformer network estimates the odometry, the change in pose between images up to scale, from SuperPoint features matched by LightGlue. The MSCKF uses these pseudo-measurements along with an orbit and attitude kinematics model to estimate the pose of the target. In particular, the relative orbit elements, the target's attitude with respect to the servicer's camera, and the associated angular velocity are estimated directly by the filter. Given the monocular approach and short distance to the target, the full observability of the range to the target is recovered via attitude maneuvers by the servicer. The method is trained and evaluated on a re-rendered high-resolution version of the SPE3R dataset, which includes synthetic images of 103 spacecraft. Eleven of these spacecraft are held out during training to evaluate the generalization to unseen targets. Monte Carlo simulations are then used to evaluate the navigation pipeline on rendered trajectories of the held out spacecraft. The results demonstrate that learned vision pipelines as a front-end for Kalman filters provide median errors of 3.7° in attitude and 2.2% of range in ROE when navigating about unknown targets.

Abstract

Spacecraft Pose Estimation

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