论文标题
对主动触觉动作选择的各种信息增益标准的经验评估，用于姿势估计
An Empirical Evaluation of Various Information Gain Criteria for Active Tactile Action Selection for Pose Estimation
论文作者
论文摘要
使用多模式感知（例如视觉和触觉传感）的准确对象姿势估计已用于文献中的自主机器人操纵器。由于视觉和触觉数据的密度变化，我们先前提出了一种新型的概率贝叶斯过滤器的方法，该方法称为翻译不变的四元素过滤器（TIQF）进行姿势估计。由于触觉数据收集很耗时，因此通过推理而不是多个潜在的动作来获得最大预期信息增益，因此优先考虑主动触觉数据收集。在本文中，我们在对象姿势估计的背景下凭经验评估了各种信息获取标准。我们证明了我们提出的TIQF姿势估计方法的适应性和有效性，并具有各种信息增益标准。在所有选定的标准中，我们发现姿势准确性的性能相似，并且稀疏测量。
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.
