2023
DOI: 10.36227/techrxiv.22122596
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Marker Displacement Method Used in Vision-Based Tactile Sensors—From 2D to 3D: A Review

Abstract: <p>This article presents a detailed review and categorizing of the marker displacement method (MDM) used in vision-based tactile sensors. Vision-based tactile sensors have been proven to be a promising solution for robot tactile perception. Among such sensors, MDM is one of the most commonly used contact characterization and extraction methods. It uses visual approaches to obtain contact deformation and achieve multimodal tactile perception using physical models and post-processing algorithms. In recent … Show more

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Cited by 2 publications
(2 citation statements)
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“…In recent years, different types of tactile sensors have been developed [4], [5]. Among them, vision-based tactile sensors (also called visuotactile sensors) [6]- [9] are a class of sensors with the advantages of high-resolution and multi-modal perception, such as GelForce [10], GelSight [11], GelSlim [12], FingerVision [13], TacTip [14], and OmniTact [15]. These sensors use a soft elastomer to contact the objects.…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, different types of tactile sensors have been developed [4], [5]. Among them, vision-based tactile sensors (also called visuotactile sensors) [6]- [9] are a class of sensors with the advantages of high-resolution and multi-modal perception, such as GelForce [10], GelSight [11], GelSlim [12], FingerVision [13], TacTip [14], and OmniTact [15]. These sensors use a soft elastomer to contact the objects.…”
Section: Introductionmentioning
confidence: 99%
“…Given the extensive volume of reports accessible on the centralized boyukgayidish.gov.az portal, obtaining detailed information efficiently becomes a challenge. In the present case, a customized chatbot [13] model, leveraging advanced natural language processing like GPT, was developed. Specific data is meticulously fine-tuning to enhance the chatbot's [10] customization and the model underwent fine-tuning to increase its accuracy in generating relevant responses.…”
Section: Introductionmentioning
confidence: 99%