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

Murali, P. K. , Dahiya, R. and Kaboli, M. (2022) An Empirical Evaluation of Various Information Gain Criteria for Active Tactile Action Selection for Pose Estimation. In: 2022 IEEE International Conference on Flexible & Printable Sensors & Systems, Vienna, Austria, 10-13 Jul 2022, ISBN 9781665442732 (doi: 10.1109/FLEPS53764.2022.9781598)

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.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Dahiya, Professor Ravinder and Murali, Prajval Kumar
• Authors: Murali, P. K., Dahiya, R., and Kaboli, M.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• ISBN: 9781665442732
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in 2022 IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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