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Accurate object pose estimation using multi-modal perception such as visualand tactile sensing have been used for autonomous robotic manipulators inliterature. Due to variation in density of visual and tactile data, wepreviously proposed a novel probabilistic Bayesian filter-based approach termedtranslation-invariant Quaternion filter (TIQF) for pose estimation. As tactiledata collection is time consuming, active tactile data collection is preferredby reasoning over multiple potential actions for maximal expected informationgain. In this paper, we empirically evaluate various information gain criteriafor action selection in the context of object pose estimation. We demonstratethe adaptability and effectiveness of our proposed TIQF pose estimationapproach with various information gain criteria. We find similar performance interms of pose accuracy with sparse measurements across all the selectedcriteria.

An Empirical Evaluation of Various Information Gain Criteria for Active Tactile Action Selection for Pose Estimation