Pose Tracking with a Foundation Pose Model and an Ensemble Directional Kalman Filter
Machine Learning
2026-05-06 v1 Differential Geometry
Applications
Abstract
This paper introduces the ensemble directional Kalman filter (EnDKF), an ensemble-based Kalman filtering approach for pose tracking that jointly estimates an object's position and attitude using ideas from directional statistics. The EnDKF integrates a unit-quaternion attitude representation to move beyond canonical Kalman filter mean and covariance assumptions that poorly capture directional uncertainty. Experiments on a synthetic constant-velocity constant-angular-velocity system and a digital-twin head-tracking scenario using the FoundationPose algorithm demonstrate a significant reduction in error as opposed to merely using measurements.
Keywords
Cite
@article{arxiv.2605.03105,
title = {Pose Tracking with a Foundation Pose Model and an Ensemble Directional Kalman Filter},
author = {Tianlu Lu and Asif Sijan and Thomas Noh and Huaijin Chen and Andrey A. Popov},
journal= {arXiv preprint arXiv:2605.03105},
year = {2026}
}