TacLoc: Global Tactile Localization on Objects from a Registration Perspective
This addresses the challenge of generalizable and efficient tactile localization in robotics, particularly under visual occlusion, though it appears incremental as it builds on existing registration techniques.
The paper tackles the problem of tactile-based pose estimation for robotic manipulation by proposing TacLoc, a one-shot point cloud registration framework that uses graph-theoretic methods and normal-guided pruning, achieving improved performance on the YCB dataset and real-world objects with different sensors.
Pose estimation is essential for robotic manipulation, particularly when visual perception is occluded during gripper-object interactions. Existing tactile-based methods generally rely on tactile simulation or pre-trained models, which limits their generalizability and efficiency. In this study, we propose TacLoc, a novel tactile localization framework that formulates the problem as a one-shot point cloud registration task. TacLoc introduces a graph-theoretic partial-to-full registration method, leveraging dense point clouds and surface normals from tactile sensing for efficient and accurate pose estimation. Without requiring rendered data or pre-trained models, TacLoc achieves improved performance through normal-guided graph pruning and a hypothesis-and-verification pipeline. TacLoc is evaluated extensively on the YCB dataset. We further demonstrate TacLoc on real-world objects across two different visual-tactile sensors.