About an Automating Annotation Method for Robot Markers
Wataru Uemura, Takeru Nagashima

TL;DR
This paper introduces an automated annotation method leveraging ArUco marker detection to train deep learning models for robot marker recognition, significantly reducing manual effort and improving robustness under challenging imaging conditions.
Contribution
The paper presents a novel automated annotation approach using ArUco markers to facilitate deep learning training, eliminating manual labeling and enhancing recognition accuracy in noisy environments.
Findings
Automated annotation reduces human labeling effort.
Deep learning models trained with this method outperform traditional techniques.
Recognition robustness improves under noise, blur, and defocus conditions.
Abstract
Factory automation has become increasingly important due to labor shortages, leading to the introduction of autonomous mobile robots for tasks such as material transportation. Markers are commonly used for robot self-localization and object identification. In the RoboCup Logistics League (RCLL), ArUco markers are employed both for robot localization and for identifying processing modules. Conventional recognition relies on OpenCV-based image processing, which detects black-and-white marker patterns. However, these methods often fail under noise, motion blur, defocus, or varying illumination conditions. Deep-learning-based recognition offers improved robustness under such conditions, but requires large amounts of annotated data. Annotation must typically be done manually, as the type and position of objects cannot be detected automatically, making dataset preparation a major bottleneck.…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Robotics and Automated Systems · Image Processing Techniques and Applications
