ContactFusion: Stochastic Poisson Surface Maps from Visual and Contact Sensing
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908276678983680 |
|---|---|
| author | Kamireddypalli, Aditya Moura, Joao Buchanan, Russell Vijayakumar, Sethu Ramamoorthy, Subramanian |
| author_facet | Kamireddypalli, Aditya Moura, Joao Buchanan, Russell Vijayakumar, Sethu Ramamoorthy, Subramanian |
| contents | Robust and precise robotic assembly entails insertion of constituent components. Insertion success is hindered when noise in scene understanding exceeds tolerance limits, especially when fabricated with tight tolerances. In this work, we propose ContactFusion which combines global mapping with local contact information, fusing point clouds with force sensing. Our method entails a Rejection Sampling based contact occupancy sensing procedure which estimates contact locations on the end-effector from Force/Torque sensing at the wrist. We demonstrate how to fuse contact with visual information into a Stochastic Poisson Surface Map (SPSMap) - a map representation that can be updated with the Stochastic Poisson Surface Reconstruction (SPSR) algorithm. We first validate the contact occupancy sensor in simulation and show its ability to detect the contact location on the robot from force sensing information. Then, we evaluate our method in a peg-in-hole task, demonstrating an improvement in the hole pose estimate with the fusion of the contact information with the SPSMap. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16592 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ContactFusion: Stochastic Poisson Surface Maps from Visual and Contact Sensing Kamireddypalli, Aditya Moura, Joao Buchanan, Russell Vijayakumar, Sethu Ramamoorthy, Subramanian Robotics Robust and precise robotic assembly entails insertion of constituent components. Insertion success is hindered when noise in scene understanding exceeds tolerance limits, especially when fabricated with tight tolerances. In this work, we propose ContactFusion which combines global mapping with local contact information, fusing point clouds with force sensing. Our method entails a Rejection Sampling based contact occupancy sensing procedure which estimates contact locations on the end-effector from Force/Torque sensing at the wrist. We demonstrate how to fuse contact with visual information into a Stochastic Poisson Surface Map (SPSMap) - a map representation that can be updated with the Stochastic Poisson Surface Reconstruction (SPSR) algorithm. We first validate the contact occupancy sensor in simulation and show its ability to detect the contact location on the robot from force sensing information. Then, we evaluate our method in a peg-in-hole task, demonstrating an improvement in the hole pose estimate with the fusion of the contact information with the SPSMap. |
| title | ContactFusion: Stochastic Poisson Surface Maps from Visual and Contact Sensing |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.16592 |