SymmeTac: Symmetric Color LED Driven Efficient Photometric Stereo Reconstruction Methods for Camera-based Tactile Sensors

TL;DR

SymmeTac introduces a fast, two-shot photometric stereo method using symmetric color LED illumination for accurate and efficient surface reconstruction in camera-based tactile sensors, enhancing robotic interaction capabilities.

Contribution

It presents a novel two-shot photometric stereo approach leveraging symmetric color LEDs and optimized algorithms for high efficiency and accuracy in tactile surface reconstruction.

Findings

Achieves accurate surface normal estimation with reduced computation.

Demonstrates superior performance in simulations and real-world tests.

Significantly improves efficiency over existing methods.

Abstract

Camera-based tactile sensors can provide high-density surface geometry and force information for robots in the interaction process with the target. However, most existing methods cannot achieve accurate reconstruction with high efficiency, impeding the applications in robots. To address these problems, we propose an efficient two-shot photometric stereo method based on symmetric color LED distribution. Specifically, based on the sensing response curve of CMOS channels, we design orthogonal red and blue LEDs as illumination to acquire four observation maps using channel-splitting in a two-shot manner. Subsequently, we develop a two-shot photometric stereo theory, which can estimate accurate surface normal and greatly reduce the computing overhead in magnitude. Finally, leveraging the characteristics of the camera-based tactile sensor, we optimize the algorithm to be a highly efficient, pure addition operation. Simulation and real-world experiments demonstrate the advantages of our approach. Further details are available on: https://github.com/Tacxels/SymmeTac.