Active Visuo-Tactile Point Cloud Registration for Accurate Pose Estimation of Objects in an Unknown Workspace

TL;DR

This paper introduces an active visuo-tactile approach for precise 3D object pose estimation in unknown environments, combining efficient linear filtering with active exploration to improve robotic manipulation accuracy.

Contribution

It presents a novel active visuo-tactile methodology with a linear quaternion filter for real-time, accurate pose estimation of objects in unknown workspaces.

Findings

High accuracy in pose estimation demonstrated in simulation.

Real-time performance achieved with computationally efficient strategy.

Effective exploration and localization in unknown environments.

Abstract

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.