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Communication Dans Un Congrès Année : 2024

A Study on Learned Feature Maps Toward Direct Visual Servoing

Résumé

Direct Visual Servoing (DVS) is a technique used in robotics and computer vision where visual information, typically obtained from camera pixels brightness, is directly used for controlling the motion of a robot. DVS is known for its ability to achieve accurate positioning, thanks to the redundancy of information all without the necessity to rely on geometric features. In this paper, we introduce a novel approach where pixel brightness is replaced with learned feature maps as the visual information for the servoing loop. The aim of this paper is to present a procedure to extract, transform and integrate deep neural networks feature maps toward replacing the brightness in a DVS control loop.
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Dates et versions

hal-04306456 , version 1 (11-01-2024)

Identifiants

  • HAL Id : hal-04306456 , version 1

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Matthieu Quaccia, Antoine André, Yusuke Yoshiyasu, Guillaume Caron. A Study on Learned Feature Maps Toward Direct Visual Servoing. 16th IEEE/SICE International Symposium on System Integration (SII2024), IEEE; SICE, Jan 2024, Ha Long, Vietnam. ⟨hal-04306456⟩
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