Real-time and Robust Feature Detection of Continuous Marker Pattern for Dense 3-D Deformation Measurement

TL;DR

This paper introduces a real-time, robust feature detection method for continuous marker patterns in visuotactile sensors, enabling high-density 3D deformation visualization and inverse sensing of dynamic contact processes.

Contribution

It presents a novel feature detection algorithm considering boundary characteristics, improving reliability and efficiency in 3D deformation measurement.

Findings

Significant real-time detection performance improvements

Enhanced robustness in boundary and geometric distortion conditions

Successful high-density 3D contact deformation visualization

Abstract

Visuotactile sensing technology has received much attention in recent years. This article proposes a feature detection method applicable to visuotactile sensors based on continuous marker patterns (CMP) to measure 3-d deformation. First, we construct the feature model of checkerboard-like corners under contact deformation, and design a novel double-layer circular sampler. Then, we propose the judging criteria and response function of corner features by analyzing sampling signals' amplitude-frequency characteristics and circular cross-correlation behavior. The proposed feature detection algorithm fully considers the boundary characteristics retained by the corners with geometric distortion, thus enabling reliable detection at a low calculation cost. The experimental results show that the proposed method has significant advantages in terms of real-time and robustness. Finally, we have achieved the high-density 3-d contact deformation visualization based on this detection method. This technique is able to clearly record the process of contact deformation, thus enabling inverse sensing of dynamic contact processes.