Pose Tracking with a Foundation Pose Model and an Ensemble Directional Kalman Filter
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909013381218304 |
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| author | Lu, Tianlu Sijan, Asif Noh, Thomas Chen, Huaijin Popov, Andrey A. |
| author_facet | Lu, Tianlu Sijan, Asif Noh, Thomas Chen, Huaijin Popov, Andrey A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03105 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Pose Tracking with a Foundation Pose Model and an Ensemble Directional Kalman Filter Lu, Tianlu Sijan, Asif Noh, Thomas Chen, Huaijin Popov, Andrey A. Machine Learning Differential Geometry Applications 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. |
| title | Pose Tracking with a Foundation Pose Model and an Ensemble Directional Kalman Filter |
| topic | Machine Learning Differential Geometry Applications |
| url | https://arxiv.org/abs/2605.03105 |