視触覚融合による把持物体の姿勢推定
In-Hand Object Pose Estimation via Visual-Tactile Fusion
手首カメラと指先の触覚センサを融合し、重み付きICPで把持物体の6D姿勢を推定する手法を提案。遮蔽時も高精度で、挿入タスクに応用した。
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著者: Felix Nonnengießer, Alap Kshirsagar, Boris Belousov, Jan Peters
分類: cs.RO
原文アブストラクト
Accurate in-hand pose estimation is crucial for robotic object manipulation, but visual occlusion remains a major challenge for vision-based approaches. This paper presents an approach to robotic in-hand object pose estimation, combining visual and tactile information to accurately determine the position and orientation of objects grasped by a robotic hand. We address the challenge of visual occlusion by fusing visual information from a wrist-mounted RGB-D camera with tactile information from vision-based tactile sensors mounted on the fingertips of a robotic gripper. Our approach employs a weighting and sensor fusion module to combine point clouds from heterogeneous sensor types and control each modality's contribution to the pose estimation process. We use an augmented Iterative Closest Point (ICP) algorithm adapted for weighted point clouds to estimate the 6D object pose. Our experiments show that incorporating tactile information significantly improves pose estimation accuracy, particularly when occlusion is high. Our method achieves an average pose estimation error of 7.5 mm and 16.7 degrees, outperforming vision-only baselines by up to 20%. We also demonstrate the ability of our method to perform precise object manipulation in a real-world insertion task.
