Depth-Based Visual Servoing Using Low-Accurate Arm

TL;DR

This paper introduces a visual-servoing approach for grasping objects using a low-accuracy robotic arm, leveraging RGB-D sensors and SURF features to achieve high success rates despite system limitations.

Contribution

It presents a novel visual-servoing method that compensates for low arm accuracy using depth segmentation and SURF features for reliable object recognition.

Findings

Achieves over 95% success rate in object grasping in real-world tests.

Uses depth segmentation to enhance recognition speed and reliability.

Employs SURF features for high frame rate object recognition.

Abstract

This paper proposes a visual-servoing method dedicated to grasping of daily-life objects. In order to obtain an affordable solution, we use a low-accurate robotic arm. Our method corrects errors by using an RGB-D sensor. It is based on SURF invariant features which allows us to perform object recognition at a high frame rate. We define regions of interest based on depth segmentation, and we use them to speed-up the recognition and to improve reliability. The system has been tested on a real-world scenario. In spite of the lack of accuracy of all the components and the uncontrolled environment, it grasps objects successfully on more than 95 percents of the trials.