触覚SLAM：平面押し動作からの形状と姿勢のリアルタイム推定
Tactile SLAM: Real-time inference of shape and pose from planar pushing
触覚センサの計測列から未知物体の形状と姿勢をリアルタイムに推定する手法を提案し、平面押しによる触覚探索をオンラインSLAM問題として定式化した。
著者: Sudharshan Suresh, Maria Bauza, Kuan-Ting Yu, Joshua G. Mangelson, Alberto Rodriguez, Michael Kaess
分類: cs.RO
原文アブストラクト
Tactile perception is central to robot manipulation in unstructured environments. However, it requires contact, and a mature implementation must infer object models while also accounting for the motion induced by the interaction. In this work, we present a method to estimate both object shape and pose in real-time from a stream of tactile measurements. This is applied towards tactile exploration of an unknown object by planar pushing. We consider this as an online SLAM problem with a nonparametric shape representation. Our formulation of tactile inference alternates between Gaussian process implicit surface regression and pose estimation on a factor graph. Through a combination of local Gaussian processes and fixed-lag smoothing, we infer object shape and pose in real-time. We evaluate our system across different objects in both simulated and real-world planar pushing tasks.
