融合视觉与接触力传感，提升机器人装配精度
ContactFusion: Stochastic Poisson Surface Maps from Visual and Contact Sensing
- 用排斥采样法从腕部力矩数据推断接触位置
- 接触信息融入随机泊松表面图，提升孔位姿态估计
- 适合高精度装配任务的机器人系统开发
可靠的机器人装配依赖于部件的精确插入。当场景理解噪声超出容忍范围时，尤其在紧密公差下，插入成功率下降。本文提出ContactFusion，结合全局地图与局部接触信息，将点云与力觉数据融合。方法采用基于排斥采样的接触占据感知过程，从腕部力/力矩传感器数据中估计末端执行器上的接触位置。我们展示了如何将接触信息与视觉信息融合到随机泊松表面图（SPSMap）中，该表示可通过随机泊松表面重建（SPSR）算法动态更新。首先在仿真中验证接触占据传感器的有效性，可准确检测机器人末端的接触位置；随后在插销入孔任务中评估，结果表明融合接触信息后，孔位姿态估计显著改善。
原文摘要 · Abstract (English)
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.
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