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.

翻译：由于视觉和触觉数据密度的差异,我们以前曾提出一种新型的贝叶斯过滤法,称为翻译变异的量子过滤法(TIQF)来进行估算。由于触觉数据收集耗时,因此,积极触摸数据收集更倾向于通过推理而不是多种潜在行动实现最大预期信息收益。在本文中,我们实证地评估了在对象构成估计的情况下选择行动的各种信息收益标准。我们用各种信息收益标准来显示我们提议的TIQF的估算方法的适应性和有效性。我们发现,在所有选定的标准中,从精确度和稀疏测量方面,我们发现相似的性表现。