  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.
