Autonomous Exploration for Shape Reconstruction and Measurement Via Informative Contact-Guided Planning

Abstract

Coordinate Measuring Machines (CMMs) are widely used for high-precision inspection of industrial parts, particularly in scenarios where visual systems are ineffective or cost-prohibitive. However, conventional CMMs rely on CAD model priors and user-defined probing paths, which limit their applicability and efficiency in measuring freeform parts. To overcome these limitations, we present a fully autonomous, CAD model-free, tactile-based framework that enables dense 3D shape reconstruction to facilitate subsequent measurements. Our approach leverages a dual Gaussian Process Implicit Surface architecture, termed Exploration-Reconstruction GPIS (ER-GPIS), which enables both high-fidelity shape reconstruction and uncertainty estimation on the object’s surface. A hybrid exploration motion planner is then employed to adaptively sample surface geometries by integrating local surface exploration, global exploration, and contact recovery policies for robust shape estimation. Extensive real-world experiments demonstrate that the proposed method effectively reconstructs object geometries across diverse shapes, highlighting its ability to autonomously reconstruct and measure both surfaces and internal features without relying on CAD model priors.