Corpus ID: 236447743

VIPose: Real-time Visual-Inertial 6D Object Pose Tracking

@article{Ge2021VIPoseRV,
  title={VIPose: Real-time Visual-Inertial 6D Object Pose Tracking},
  author={Rundong Ge and Giuseppe Loianno},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.12617}
}
Estimating the 6D pose of objects is beneficial for robotics tasks such as transportation, autonomous navigation, manipulation as well as in scenarios beyond robotics like virtual and augmented reality. With respect to single image pose estimation, pose tracking takes into account the temporal information across multiple frames to overcome possible detection inconsistencies and to improve the pose estimation efficiency. In this work, we introduce a novel Deep Neural Network (DNN) called VIPose… Expand

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References

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TLDR
This work proposes a data-driven optimization approach for long-term, 6D pose tracking, which aims to identify the optimal relative pose given the current RGB-D observation and a synthetic image conditioned on the previous best estimate and the object’s model. Expand
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TLDR
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