Robust Object Pose Tracking for Augmented Reality Guidance and Teleoperation
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".