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

TL;DR

This paper empirically evaluates different information gain criteria for active tactile action selection in object pose estimation, demonstrating the effectiveness of the proposed TIQF approach with various criteria.

Contribution

It introduces an empirical comparison of information gain criteria for active tactile data collection in pose estimation, validating the robustness of the TIQF method.

Findings

Similar pose accuracy across different information gain criteria

TIQF approach effectively integrates multi-modal perception

Active tactile data collection benefits from optimized information gain

Abstract

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.