物体姿勢推定のための能動的触覚行動選択における情報利得基準の実証評価
An Empirical Evaluation of Various Information Gain Criteria for Active Tactile Action Selection for Pose Estimation
物体姿勢推定のための触覚データ収集において、複数の情報利得基準を比較し、疎な計測でも姿勢精度が同程度になることを示した。
著者: Prajval Kumar Murali, Ravinder Dahiya, Mohsen Kaboli
分類: cs.RO
原文アブストラクト
Accurate object pose estimation using multi-modal perception such as visual and tactile sensing have been used for autonomous robotic manipulators in literature. Due to variation in density of visual and tactile data, we previously proposed a novel probabilistic Bayesian filter-based approach termed translation-invariant Quaternion filter (TIQF) for pose estimation. As tactile data collection is time consuming, active tactile data collection is preferred by reasoning over multiple potential actions for maximal expected information gain. In this paper, we empirically evaluate various information gain criteria for action selection in the context of object pose estimation. We demonstrate the adaptability and effectiveness of our proposed TIQF pose estimation approach with various information gain criteria. We find similar performance in terms of pose accuracy with sparse measurements across all the selected criteria.
