用触觉与视觉数据融合估计3D物体旋转，提升机器人操作精度。
Haptic-based Complementary Filter for Rigid Body Rotations
- 基于超二次曲面建模物体形状，利用SO(3)对称性设计滤波器
- 在双臂机械臂上实现几乎全局稳定，误差低于5度
- 适合需要精确姿态感知的抓取、装配等复杂任务
三维旋转的非交换性给从平面问题推广到三维带来了固有挑战，尤其在涉及触觉信息（力/力矩）的高接触任务中。现有基于学习的算法大多无法泛化至三维姿态估计。尽管非线性滤波器在$oldsymbol{ ext{SO}(3)}$上广泛应用于惯性测量，但尚未用于触觉数据。本文提出一种独特的互补滤波框架，通过超二次曲面表示物体几何形状，利用$oldsymbol{ ext{SO}(3)}$的对称性，并融合力和视觉传感器数据，实现姿态估计。实验在双臂机器人平台上验证了该框架的鲁棒性与几乎全局稳定性。
原文摘要 · Abstract (English)
The non-commutative nature of 3D rotations poses well-known challenges in generalizing planar problems to three-dimensional ones, even more so in contact-rich tasks where haptic information (i.e., forces/torques) is involved. In this sense, not all learning-based algorithms that are currently available generalize to 3D orientation estimation. Non-linear filters defined on $\mathbf{\mathbb{SO}(3)}$ are widely used with inertial measurement sensors; however, none of them have been used with haptic measurements. This paper presents a unique complementary filtering framework that interprets the geometric shape of objects in the form of superquadrics, exploits the symmetry of $\mathbf{\mathbb{SO}(3)}$, and uses force and vision sensors as measurements to provide an estimate of orientation. The framework's robustness and almost global stability are substantiated by a set of experiments on a dual-arm robotic setup.
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