UPETrack: Unidirectional Position Estimation for Tracking Occluded Deformable Linear Objects
🤖 AI Summary
Real-time tracking of deformable linear objects (DLOs) is fundamentally challenging due to their high-dimensional configuration space, strongly nonlinear dynamics, and frequent partial occlusions. This paper proposes a model-free, markerless real-time tracking method: first, a Gaussian Mixture Model (GMM) is constructed from the point cloud of the visible segment to capture local geometry; second, a novel Unidirectional Position Estimation (UPE) algorithm is introduced, which leverages geometric continuity and historical curvature modeling to derive a closed-form deformation extrapolation—without requiring physical modeling, simulation, or iterative optimization; finally, robust occluded-segment state prediction is achieved by jointly enforcing proximal linearity constraints and local displacement composition. Evaluated under diverse occlusion scenarios, the method achieves superior localization accuracy and computational efficiency compared to TrackDLO and CDCPD2, enabling stable, millisecond-level tracking.
Application Category
📝 Abstract
Real-time state tracking of Deformable Linear Objects (DLOs) is critical for enabling robotic manipulation of DLOs in industrial assembly, medical procedures, and daily-life applications. However, the high-dimensional configuration space, nonlinear dynamics, and frequent partial occlusions present fundamental barriers to robust real-time DLO tracking. To address these limitations, this study introduces UPETrack, a geometry-driven framework based on Unidirectional Position Estimation (UPE), which facilitates tracking without the requirement for physical modeling, virtual simulation, or visual markers. The framework operates in two phases: (1) visible segment tracking is based on a Gaussian Mixture Model (GMM) fitted via the Expectation Maximization (EM) algorithm, and (2) occlusion region prediction employing UPE algorithm we proposed. UPE leverages the geometric continuity inherent in DLO shapes and their temporal evolution patterns to derive a closed-form positional estimator through three principal mechanisms: (i) local linear combination displacement term, (ii) proximal linear constraint term, and (iii) historical curvature term. This analytical formulation allows efficient and stable estimation of occluded nodes through explicit linear combinations of geometric components, eliminating the need for additional iterative optimization. Experimental results demonstrate that UPETrack surpasses two state-of-the-art tracking algorithms, including TrackDLO and CDCPD2, in both positioning accuracy and computational efficiency.
Problem
Research questions and friction points this paper is trying to address.
Tracks deformable linear objects in real-time despite occlusions.
Estimates occluded positions using geometric continuity without physical models.
Improves accuracy and efficiency over existing state-of-the-art methods.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Geometry-driven framework without physical modeling or markers
Two-phase tracking with GMM for visible segments and UPE for occlusions
Closed-form estimator using geometric continuity and temporal patterns
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