# Pose Tracking with a Foundation Pose Model and an Ensemble Directional Kalman Filter

arXiv: https://arxiv.org/abs/2605.03105 · HTML: https://mlsanity.com/paper/2605.03105/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 4 May 2026.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [Native and Compact Structured Latents for 3D Generation](https://mlsanity.com/paper/2512.14692/) (2025)
- [BlenderProc](https://mlsanity.com/paper/1911.01911/) (2019)
- [Spherical Text Embedding](https://mlsanity.com/paper/1911.01196/) (2019)
- [Recent advances in directional statistics](https://mlsanity.com/paper/2005.06889/) (2020)
- [Directional Statistics and Filtering Using libDirectional](https://mlsanity.com/paper/1712.09718/) (2017)
- [Preserving Nonlinear Constraints in Variational Flow Filtering Data Assimilation](https://mlsanity.com/paper/2405.04380/) (2024)
- [The Geometry of Extended Kalman Filters on Manifolds with Affine Connection](https://mlsanity.com/paper/2506.05728/) (2025)

## Abstract (arXiv)

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.