NormalFlow: Fast, Robust, and Accurate Contact-based Object 6DoF Pose Tracking with Vision-based Tactile Sensors
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917959340916736 |
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| author | Huang, Hung-Jui Kaess, Michael Yuan, Wenzhen |
| author_facet | Huang, Hung-Jui Kaess, Michael Yuan, Wenzhen |
| contents | Tactile sensing is crucial for robots aiming to achieve human-level dexterity. Among tactile-dependent skills, tactile-based object tracking serves as the cornerstone for many tasks, including manipulation, in-hand manipulation, and 3D reconstruction. In this work, we introduce NormalFlow, a fast, robust, and real-time tactile-based 6DoF tracking algorithm. Leveraging the precise surface normal estimation of vision-based tactile sensors, NormalFlow determines object movements by minimizing discrepancies between the tactile-derived surface normals. Our results show that NormalFlow consistently outperforms competitive baselines and can track low-texture objects like table surfaces. For long-horizon tracking, we demonstrate when rolling the sensor around a bead for 360 degrees, NormalFlow maintains a rotational tracking error of 2.5 degrees. Additionally, we present state-of-the-art tactile-based 3D reconstruction results, showcasing the high accuracy of NormalFlow. We believe NormalFlow unlocks new possibilities for high-precision perception and manipulation tasks that involve interacting with objects using hands. The video demo, code, and dataset are available on our website: https://joehjhuang.github.io/normalflow. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09617 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NormalFlow: Fast, Robust, and Accurate Contact-based Object 6DoF Pose Tracking with Vision-based Tactile Sensors Huang, Hung-Jui Kaess, Michael Yuan, Wenzhen Robotics Tactile sensing is crucial for robots aiming to achieve human-level dexterity. Among tactile-dependent skills, tactile-based object tracking serves as the cornerstone for many tasks, including manipulation, in-hand manipulation, and 3D reconstruction. In this work, we introduce NormalFlow, a fast, robust, and real-time tactile-based 6DoF tracking algorithm. Leveraging the precise surface normal estimation of vision-based tactile sensors, NormalFlow determines object movements by minimizing discrepancies between the tactile-derived surface normals. Our results show that NormalFlow consistently outperforms competitive baselines and can track low-texture objects like table surfaces. For long-horizon tracking, we demonstrate when rolling the sensor around a bead for 360 degrees, NormalFlow maintains a rotational tracking error of 2.5 degrees. Additionally, we present state-of-the-art tactile-based 3D reconstruction results, showcasing the high accuracy of NormalFlow. We believe NormalFlow unlocks new possibilities for high-precision perception and manipulation tasks that involve interacting with objects using hands. The video demo, code, and dataset are available on our website: https://joehjhuang.github.io/normalflow. |
| title | NormalFlow: Fast, Robust, and Accurate Contact-based Object 6DoF Pose Tracking with Vision-based Tactile Sensors |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.09617 |