Active Visuo-Tactile Point Cloud Registration for Accurate Pose Estimation of Objects in an Unknown Workspace
This addresses the challenge of precise object localization for robotics in unstructured environments, representing an incremental improvement through a novel hybrid approach.
The paper tackles the problem of accurately estimating the pose of objects in an unknown workspace for autonomous robotic manipulators by proposing an active visuo-tactile method, achieving efficient real-time performance with sparse point clouds.
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.