# Towards Design and Development of an ArUco Markers-Based Quantitative Surface Tactile Sensor

arXiv: https://arxiv.org/abs/2310.08398 · HTML: https://mlsanity.com/paper/2310.08398/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 12 Oct 2023.

## Builds on (most cited first, 19 of 19)

- [DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation](https://mlsanity.com/paper/2005.14679/) (2020)
- [GelSight Wedge: Measuring High-Resolution 3D Contact Geometry with a Compact Robot Finger](https://mlsanity.com/paper/2106.08851/) (2021)
- [Improved GelSight Tactile Sensor for Measuring Geometry and Slip](https://mlsanity.com/paper/1708.00922/) (2017)
- [OmniTact: A Multi-Directional High Resolution Touch Sensor](https://mlsanity.com/paper/2003.06965/) (2020)
- [Slip Detection with Combined Tactile and Visual Information](https://mlsanity.com/paper/1802.10153/) (2018)
- [Shape-independent Hardness Estimation Using Deep Learning and a GelSight Tactile Sensor](https://mlsanity.com/paper/1704.03955/) (2017)
- [GelTip: A Finger-shaped Optical Tactile Sensor for Robotic Manipulation](https://mlsanity.com/paper/2008.05404/) (2020)
- [DenseTact: Optical Tactile Sensor for Dense Shape Reconstruction](https://mlsanity.com/paper/2201.01367/) (2022)
- [GelSight Fin Ray: Incorporating Tactile Sensing into a Soft Compliant Robotic Gripper](https://mlsanity.com/paper/2204.07146/) (2022)
- [DTact: A Vision-Based Tactile Sensor that Measures High-Resolution 3D Geometry Directly from Darkness](https://mlsanity.com/paper/2209.13916/) (2022)
- [Digger Finger: GelSight Tactile Sensor for Object Identification Inside Granular Media](https://mlsanity.com/paper/2102.10230/) (2021)
- [DelTact: A Vision-based Tactile Sensor Using Dense Color Pattern](https://mlsanity.com/paper/2202.02179/) (2022)
- [HySenSe: A Hyper-Sensitive and High-Fidelity Vision-Based Tactile Sensor](https://mlsanity.com/paper/2211.04571/) (2022)
- [Shared-Control Robotic Manipulation in Virtual Reality](https://mlsanity.com/paper/2205.10564/) (2022)
- [GelSight EndoFlex: A Soft Endoskeleton Hand with Continuous High-Resolution Tactile Sensing](https://mlsanity.com/paper/2303.17935/) (2023)
- [Classification of Colorectal Cancer Polyps via Transfer Learning and Vision-Based Tactile Sensing](https://mlsanity.com/paper/2211.04573/) (2022)
- [Towards Reliable Colorectal Cancer Polyps Classification via Vision Based Tactile Sensing and Confidence-Calibrated Neural Networks](https://mlsanity.com/paper/2304.13192/) (2023)
- [A Smart Handheld Edge Device for On-Site Diagnosis and Classification of Texture and Stiffness of Excised Colorectal Cancer Polyps](https://mlsanity.com/paper/2309.09642/) (2023)
- [Design and Development of a Novel Soft and Inflatable Tactile Sensing Balloon for Early Diagnosis of Colorectal Cancer Polyps](https://mlsanity.com/paper/2309.09651/) (2023)

## Abstract (arXiv)

In this paper, with the goal of quantifying the qualitative image outputs of a Vision-based Tactile Sensor (VTS), we present the design, fabrication, and characterization of a novel Quantitative Surface Tactile Sensor (called QS-TS). QS-TS directly estimates the sensor's gel layer deformation in real-time enabling safe and autonomous tactile manipulation and servoing of delicate objects using robotic manipulators. The core of the proposed sensor is the utilization of miniature 1.5 mm x 1.5 mm synthetic square markers with inner binary patterns and a broad black border, called ArUco Markers. Each ArUco marker can provide real-time camera pose estimation that, in our design, is used as a quantitative measure for obtaining deformation of the QS-TS gel layer. Moreover, thanks to the use of ArUco markers, we propose a unique fabrication procedure that mitigates various challenges associated with the fabrication of the existing marker-based VTSs and offers an intuitive and less-arduous method for the construction of the VTS. Remarkably, the proposed fabrication facilitates the integration and adherence of markers with the gel layer to robustly and reliably obtain a quantitative measure of deformation in real-time regardless of the orientation of ArUco Markers. The performance and efficacy of the proposed QS-TS in estimating the deformation of the sensor's gel layer were experimentally evaluated and verified. Results demonstrate the phenomenal performance of the QS-TS in estimating the deformation of the gel layer with a relative error of <5%.