Abstract
This study employs the Perspective-n-Point (PnP) method for estimating the six degrees of freedom (6DoF) pose of a store. The 6DoF framework encompasses both translational and rotational components along three axes. A Computational Fluid Dynamics (CFD)-based simulation provides the reference 6DoF motion data. Camera calibration is performed using images of a chessboard pattern captured from various angles in order to determine the intrinsic parameters. The store is equipped with markers, whose two-dimensional positions are tracked across video frames. The PnP algorithm is applied using the tracked markers and the calibrated camera parameters to estimate the pose of the store, and the resulting estimates are refined using an Exponential Moving Average (EMA) filter. The accuracy of the pose estimation is evaluated by comparing the results against CFD data using the Root Mean Square Error (RMSE) metric. This work systematically analyzes the performance of the PnP-based pose estimation approach by benchmarking it against high-fidelity simulation data.
| Translated title of the contribution | PnP-Based 6DoF Pose Estimation with Marker Tracking and CFD Simulation Comparison |
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| Original language | Turkish |
| Title of host publication | 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798331566555 |
| DOIs | |
| Publication status | Published - 2025 |
| Event | 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Istanbul, Turkey Duration: 25 Jun 2025 → 28 Jun 2025 |
Publication series
| Name | 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings |
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Conference
| Conference | 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 |
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| Country/Territory | Turkey |
| City | Istanbul |
| Period | 25/06/25 → 28/06/25 |
Bibliographical note
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