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Jingyi Xiang, Holly Dinkel, Harry Zhao, Naixiang Gao, Brian Coltin, Trey Smith, and Timothy Bretl. IEEE Robotics and Automation Letters, 8(10):6179–6186, 2023.
The TrackDLO algorithm estimates the shape of a Deformable Linear Object (DLO) under occlusion from a sequence of RGB-D images. TrackDLO is vision-only and runs in real-time. It requires no external state information from physics modeling, simulation, visual markers, or contact as input. The algorithm improves on previous approaches by addressing three common scenarios which cause tracking failure: tip occlusion, mid-section occlusion, and self-occlusion. This is achieved through the application of Motion Coherence Theory to impute the spatial velocity of occluded nodes, the use of the topological geodesic distance to track self-occluding DLOs, and the introduction of a non-Gaussian kernel that only penalizes lower-order spatial displacement derivatives to reflect DLO physics. Improved real-time DLO tracking under mid-section occlusion, tip occlusion,and self-occlusion is demonstrated experimentally. The source code and demonstration data are publicly released.
@Article{xiang23:trackdlo,
author={Jingyi Xiang and Holly Dinkel and Harry Zhao and Naixiang Gao and Brian Coltin and Trey Smith and Timothy Bretl},
journal={IEEE Robotics and Automation Letters},
title={{TrackDLO}: Tracking Deformable Linear Objects Under Occlusion With Motion Coherence},
year={2023},
volume={8},
number={10},
pages={6179--6186},
doi={10.1109/LRA.2023.3303710}
}
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