InvariantCloud: A Globally Invariant, Uniquely Indexed Point Cloud Framework for Robust 6-DoF Tactile Pose Tracking
Recent advances in imitation learning and vision-language models highlight the need for high-fidelity tactile perception, with 6-DoF tactile object pose estimation providing a crucial foundation for precise robotic manipulation. We introduce InvariantCloud, a 6-DoF pose estimation framework that leverages the global invariance of surface marker constellations on vision-based tactile sensors. In contrast to recent approaches, our one-shot globally invariant point cloud registration suppresses cumulative drift and overcomes long-standing limitations in accurately estimating yaw (Z-axis) rotation. Experimental verifications show that InvariantCloud achieves superior yaw tracking accuracy and re-localization repeatability compared to existing benchmarks, demonstrating its precision and robustness in long-sequence manipulation tasks.
Code (0)
등록된 구현이 없습니다.
Tasks
Point Cloud RegistrationPose EstimationPose TrackingSimilar Papers 제목 키워드 기반
RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration
Successful point cloud registration relies on accurate correspondences established upon powerful descriptors. However, existing neural descriptors either leverage a rotation-variant backbone whose performance declines un…
Point Cloud RegistrationA perspective on Attitude Control Issues and Techniques
This paper reviews the attitude control problems for rigid-body systems, starting from the attitude representation for rigid body kinematics. Highly redundant rotation matrix defines the attitude orientation globally and…
Translation Invariant Global Estimation of Heading Angle Using Sinogram of LiDAR Point Cloud
Global point cloud registration is an essential module for localization, of which the main difficulty exists in estimating the rotation globally without initial value. With the aid of gravity alignment, the degree of fre…
Point Cloud RegistrationTranslationGMatch: Geometry-Constrained Feature Matching for RGB-D Object Pose Estimation
We present GMatch, a learning-free feature matcher designed for robust 6DoF object pose estimation, addressing common local ambiguities in sparse feature matching. Unlike traditional methods that rely solely on descripto…
GPUPose EstimationGradient Descent Only Converges to Minimizers: Non-Isolated Critical Points and Invariant Regions
Given a non-convex twice differentiable cost function f, we prove that the set of initial conditions so that gradient descent converges to saddle points where \nabla^2 f has at least one strictly negative eigenvalue has …
Open-Ended Question Answering