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%.

本文介绍了一种新型量化表面触觉传感器（称为 QS-TS），该传感器利用 ArUco 标记实时估计凝胶层的变形，能够安全和自主地使用机器人操纵器进行触觉操作和伺服精细物体。实验结果展示了 QS-TS 在估计凝胶层变形方面出色的性能，相对误差小于 5%。

基于 ArUco 标记的表面触觉传感器的设计与开发

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

In recent years, there has been a growing trend of using data-driven methods
in industrial settings. These kinds of methods often process video images or
parts, therefore the integrity of such images is crucial. Sometimes datasets,
e.g. consisting of images, can be sophisticated for various reasons. It becomes
critical to understand how the manipulation of video and images can impact the
effectiveness of a machine learning method. Our case study aims precisely to
analyze the Linemod dataset, considered the state of the art in 6D pose
estimation context. That dataset presents images accompanied by ArUco markers;
it is evident that such markers will not be available in real-world contexts.
We analyze how the presence of the markers affects the pose estimation
accuracy, and how this bias may be mitigated through data augmentation and
other methods. Our work aims to show how the presence of these markers goes to
modify, in the testing phase, the effectiveness of the deep learning method
used. In particular, we will demonstrate, through the tool of saliency maps,
how the focus of the neural network is captured in part by these ArUco markers.
Finally, a new dataset, obtained by applying geometric tools to Linemod, will
be proposed in order to demonstrate our hypothesis and uncovering the bias. Our
results demonstrate the potential for bias in 6DOF pose estimation networks,
and suggest methods for reducing this bias when training with markers.

本文通过分析 Linemod 数据集中伴随 ArUco 标记的图像噪声，探讨其对于机器学习算法 6DOF 姿态估计的影响，并提出了通过数据增强和其他方法减少偏差的方案。同时，本文证明了标记对于神经网络实现 6DOF 姿态估计具有一定的影响因素。