用低分辨率触觉+视觉提升机器人手抓物3D姿态估计精度
Visuo-Tactile Object Pose Estimation for a Multi-Finger Robot Hand with Low-Resolution In-Hand Tactile Sensing
- 融合视觉、本体感知与手指内侧二值触觉信号
- 高遮挡下姿态误差降低，实测平均13.3Hz运行速度
- 适合需在遮挡中精准操作的机械手应用
准确估计被夹持物体的3D姿态是机器人完成装配或手内操作的前提，但机器人自身手部遮挡大大增加了感知难度。本文提出将视觉信息、本体感知与机器人关节内表面分布的二值低分辨率触觉接触测量相结合，以缓解该问题。将视觉-触觉物体姿态估计建模为因子图中的概率优化问题，采用鲁棒代价函数优化物体姿态，使其与三类传感器测量对齐，降低视觉或触觉异常读数的影响。首先在仿真中验证：自研15自由度机器人手每关节配备一个二值触觉传感器，配合RGB-D相机抓取17种YCB物体。低分辨率触觉信息显著提升了高遮挡和高视觉噪声下的姿态估计性能。此外，在初步实物版本上进行抓握测试，平均实现约13.3 Hz的合理视觉-触觉姿态估计。
原文摘要 · Abstract (English)
Accurate 3D pose estimation of grasped objects is an important prerequisite for robots to perform assembly or in-hand manipulation tasks, but object occlusion by the robot's own hand greatly increases the difficulty of this perceptual task. Here, we propose that combining visual information and proprioception with binary, low-resolution tactile contact measurements from across the interior surface of an articulated robotic hand can mitigate this issue. The visuo-tactile object-pose-estimation problem is formulated probabilistically in a factor graph. The pose of the object is optimized to align with the three kinds of measurements using a robust cost function to reduce the influence of visual or tactile outlier readings. The advantages of the proposed approach are first demonstrated in simulation: a custom 15-DoF robot hand with one binary tactile sensor per link grasps 17 YCB objects while observed by an RGB-D camera. This low-resolution in-hand tactile sensing significantly improves object-pose estimates under high occlusion and also high visual noise. We also show these benefits through grasping tests with a preliminary real version of our tactile hand, obtaining reasonable visuo-tactile estimates of object pose at approximately 13.3 Hz on average.
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