RoPT: Robust 6DoF Object Pose Tracking in Video
We present RoPT, a robust tracking framework engineered to overcome this limitation by unifying visual perception, temporal kinematics, and global re-localization into a tightly coupled closed loop.
ProofPaper ↗
Key points
- Accurate 6-DoF object pose tracking is essential for robotic interaction, yet conventional monocular pipelines frequently suffer from catastrophic divergence under rapid object motion.
- Unlike existing open-loop trackers, RoPT delivers three critical capabilities that fundamentally alter system resilience.
- Second, a kinematic factor graph regularizes frame-to-frame updates, forecasts forward pose predictions, and drives a parallel multi-hypothesis verification scheme to bypass local minima.
- We demonstrate the effectiveness of our framework across challenging benchmarks alongside real-world robotic demonstrations, achieving significantly higher tracking survivability under rapid motion.
Sources (1)
- [1]RoPT: Robust 6DoF Object Pose Tracking in VideoarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 10:25 PM
We present RoPT, a robust tracking framework engineered to overcome this limitation by unifying visual perception, temporal kinematics, and global re-localization into a tightly coupled closed loop.
Accurate 6-DoF object pose tracking is essential for robotic interaction, yet conventional monocular pipelines frequently suffer from catastrophic divergence under rapid object motion.
Extractive summary: sentences quoted from the sources.