Problem
Research questions and friction points this paper is trying to address.
6D pose tracking
rigid objects
long-horizon tracking
real-time performance
Fast Pose Tracking of Rigid Objects with Compact Pose Graph Optimization
🤖 AI Summary
This study addresses the limitation of existing 6D pose tracking methods that rely on costly initialization or real-time reconstruction, thereby struggling to meet the real-time demands of robotic manipulation and augmented reality. To this end, this work proposes a lightweight framework for long-term rigid object tracking. Departing from conventional point-based optimization paradigms, the proposed method constructs a compact pose graph modeled exclusively with relative pose constraints weighted by geometrically aligned uncertainties, effectively decoupling computational complexity from the number of correspondences. Evaluated across four real-world benchmarks, the approach achieves accuracy comparable to reconstruction-based trackers at minimal optimization cost, offering an efficient and robust solution for real-time applications.
📝 Abstract
Tracking a novel object's 6D pose over long horizons currently requires either expensive onboarding or a reconstruction maintained throughout the sequence. This makes current trackers impractical for robotic manipulation and augmented reality, which need trackers that are ready to use and run in real time. 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. 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. Across four real-world benchmarks, our approach achieves tracking accuracy comparable to reconstruction-based trackers with a fraction of the optimization cost. Overall, these results suggest that a compact and reliable pose graph optimization can provide long-horizon consistency at substantially lower computational cost.
Innovation
Methods, ideas, or system contributions that make the work stand out.
6D pose tracking
compact pose graph optimization
relative pose constraints
uncertainty weighting
long-horizon consistency
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