Enhance Vision-based Tactile Sensors via Dynamic Illumination and Image Fusion

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Enhance Vision-based Tactile Sensors via Dynamic Illumination and Image Fusion

Abstract- Vision-based tactile sensors use structured light to measure deformation in their elastomeric interface. Until now, vision-based tactile sensors such as DIGIT and GelSight have been using a single, static pattern of structured light tuned to the specific form factor of the sensor. In this work, we investigate the effectiveness of dynamic illumination patterns, in conjunction with image fusion techniques, to improve the quality of sensing of vision-based tactile sensors. Specifically, we propose to capture multiple measurements, each with a different illumination pattern, and then fuse them together to obtain a single, higher-quality measurement. Experimental results demonstrate that this type of dynamic illumination yields significant improvements in image contrast, sharpness, and background difference. This discovery opens the possibility of retroactively improving the sensing quality of existing vision-based tactile sensors with a simple software update, and for new hardware designs capable of fully exploiting dynamic illumination.

One. INTRODUCTION

In robotics, haptic exploration is central to understanding the world through touch interactions. Tactile sensors allow robots to collect essential information about their surroundings, precisely manipulate objects, and ensure safe interactions within dynamic environments. By detecting physical contact, tactile sensing allows robots to avoid collisions, adjust movements, and handle objects delicately, especially in tasks that require fine interactions.

Vision-based Tactile Sensors (VBTS) are a popular choice of tactile sensors. They enable robots to perceive their environment by capturing surface deformations upon contact with objects, thus facilitating the measurement of forces, textures, and shapes. VBTS typically incorporate structured light in their construction, and currently, all such sensors use static illumination, meaning the lighting intensity and colors remain constant during measurements.

Enhancing images from VBTS holds pivotal importance due to their widespread applicability across diverse robotic tasks. These sensors serve as crucial components in robotic systems, providing essential data for various operations. The state-of-the-art approach involves training deep neural networks using images from VBTS, where the quality of the input image significantly influences the model's performance and output. Improved imaging quality from VBTS could offer deeper insights into robotic interactions with objects, ultimately enhancing problem-solving capabilities. Addressing this need, our study aims to explore the feasibility of image enhancement in VBTS and propose methodologies for achieving this enhancement.

In this study, we contribute to the field by establishing a framework to enhance the measurement quality of vision-based tactile sensors through the application of dynamic lighting and image fusion techniques. Our investigation delves into the mathematical formulation of this framework, and the comprehensive evaluation and demonstration of diverse approaches tailored to enhance image quality. Specifically, our methodology integrates dynamic lighting schemes to enhance contrast and sharpness, while employing image fusion algorithms to combine multiple sensor outputs into cohesive images. We further validate the feasibility of enhancing sensor images and conduct a comparative analysis of various illumination variations and image fusion methods, assessing their applicability to vision-based tactile sensors. Through rigorous experimentation and analysis, we present a spectrum of effective techniques poised to enhance images acquired from VBTS.

The development of techniques for enhancing images from VBTS holds promise in advancing the capabilities of robotic systems. By improving image quality, this research equips robots with deeper insights into their interactions with objects, thereby enhancing their problem-solving abilities across a set of tasks. Our systematic exploration and validation of these enhancement techniques lay a solid foundation for the integration of advanced imaging capabilities into robotic systems. This paves the way for more efficient and effective robotic applications in various real-world scenarios,

thereby contributing significantly to the advancement of robotics technology.

Our contributions are:

We introduce an approach of dynamic lighting for vision-based tactile sensors and demonstrate the methodology for its usage.

We show that it is possible to enhance the measurements from the sensor using dynamic lighting and image fusion techniques.

We identify the most effective image fusion method to be used in conjunction with dynamic lighting.

We determine the number of images for optimal output image quality.

We analyze the time required to apply dynamic lighting effectively.

II. RELATED WORK

B. Active Illumination for Photogrammetry

C. Image Fusion

Three. BACKGROUND

B. Image Fusion

Four. Dynamic Illumination for Vision-Based Tactile Sensors

B. Metrics

V. EXPERIMENTAL RESULTS

A. Proof-of-concept

B. Data Collection

C. Enhancing Image Quality

D. Metrics and the most effective method

E. Experimental Results

F. Number of Images

G. Time and image quality

Overview

The paper investigates methods to enhance the performance of vision-based tactile sensors by employing dynamic lighting and image fusion strategies to produce higher-quality images. It provides a framework and evaluates the feasibility of various approaches to improve sensing capabilities in robotics.

Key Points

1Dynamic illumination significantly improves image contrast and sharpness
2The combination of multiple illumination patterns enhances overall measurement quality
3Image fusion techniques can integrate diverse sensor outputs for superior image clarity
4The study establishes a framework for improving vision-based tactile sensor performance through software updates
5Experimental results demonstrate the effectiveness of the proposed methodologies in real-world robotic applications.

Details

Authors: Artemii Redkin, Zdravko Dugonjic, Mike Lambeta, Roberto Calandra
Category: Technology and Engineering

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