  This paper proposes a novel active visuo-tactile based methodology wherein
the accurate estimation of the time-invariant SE(3) pose of objects is
considered for autonomous robotic manipulators. The robot equipped with tactile
sensors on the gripper is guided by a vision estimate to actively explore and
localize the objects in the unknown workspace. The robot is capable of
reasoning over multiple potential actions, and execute the action to maximize
information gain to update the current belief of the object. We formulate the
pose estimation process as a linear translation invariant quaternion filter
(TIQF) by decoupling the estimation of translation and rotation and formulating
the update and measurement model in linear form. We perform pose estimation
sequentially on acquired measurements using very sparse point cloud as
acquiring each measurement using tactile sensing is time consuming.
Furthermore, our proposed method is computationally efficient to perform an
exhaustive uncertainty-based active touch selection strategy in real-time
without the need for trading information gain with execution time. We evaluated
the performance of our approach extensively in simulation and by a robotic
system.
