Fast Pose Tracking of Rigid Objects with Compact Pose Graph Optimization
We show that a lightweight tracking module can be applied on top of a wide range of correspondence estimation methods to keep drifts bounded while maintaining fast runtime.
ProofPaper ↗
Key points
- Tracking a novel object's 6D pose over long horizons currently requires either expensive onboarding or a reconstruction maintained throughout the sequence.
- Our key idea is to avoid point-based optimization in the pose graph and operate only on relative pose constraints, which we weight by a derived uncertainty from the geometric alignment.
- This makes optimization independent of the number of correspondences while avoiding the direct inclusion of noisy point measurements, leading to fast and robust long-term tracking.
- Overall, these results suggest that a compact and reliable pose graph optimization can provide long-horizon consistency at substantially lower computational cost.
Sources (1)
- [1]Fast Pose Tracking of Rigid Objects with Compact Pose Graph OptimizationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 12:21 PM
We show that a lightweight tracking module can be applied on top of a wide range of correspondence estimation methods to keep drifts bounded while maintaining fast runtime.
Tracking a novel object's 6D pose over long horizons currently requires either expensive onboarding or a reconstruction maintained throughout the sequence.
Extractive summary: sentences quoted from the sources.