Robust Object Pose Tracking for Augmented Reality Guidance and Teleoperation
Why this work is in the frame
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Bibliographic record
Abstract
For many augmented reality guidance, teleoperation, or human-robot interaction systems, accurate, fast, and robust 6 degree of freedom object pose tracking is essential. However, current solutions easily lose tracking when line-of-sight to markers is lost. In this paper we present a tracking system which matches or improves on current methods in speed and accuracy, achieving 1.77 mm and 1.51 degrees accuracy at 22 Hz, and is robust to occlusions. Reflective markers are segmented in infrared images and used for pose computation using novel voting-based point correspondence algorithms and intelligent cropping. Additionally, we introduce a new square-root unscented Kalman filter which improves accuracy and flexibility over previous approaches by tracking the markers themselves rather than the computed pose and enabling fusion of an external inertial measurement unit. This reduces noise and makes the tracking robust to brief loss of line-of-sight. The algorithms and methods are described in detail with pseudo-code, tested, and analyzed. The system is implemented in simulation and on a Microsoft HoloLens 2 using Unity for ease of integration into graphical projects. The code is made available open source. Through the improvements in speed and robustness over previous methods, this solution has the potential to enable fast and reliable pose tracking for many mixed reality and teleoperation applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it