A Machine Learning Approach to Robot Localization Using Fiducial Markers in RobotAtFactory 4.0 Competition

Klein, Luan Carlos; Braun, João; Mendes, João; Pinto, Vítor H.; Martins, Felipe Nascimento; Oliveira, André Schneider de; Wörtche, H. J.; Costa, Paulo; Lima, José

doi:10.3390/s23063128

Cited by 7 publications

(1 citation statement)

References 42 publications

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“…Furthermore, a comprehensive evaluation of fiducial marker systems reveals their diverse applicability across robotics applications [18]. Machine learning approaches for robot localization using fiducial markers are introduced by Klein et al [19], achieving millimeter-scale error in pose estimation. Kim et al [20] and Ekici et al [21] present novel solutions for localization and object tracking in dynamic environments, utilizing multi-layered 3D scan-matching, virtual fiducial markers and robust object pose tracking algorithms.…”

Section: Related Workmentioning

confidence: 99%

Kabsch Marker Estimation Algorithm—A Multi-Robot Marker-Based Localization Algorithm Within the Industry 4.0 Context

Braun,

Lima,

Pereira

et al. 2024

IEEE Access

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This paper introduces the Kabsch Marker Estimation Algorithm (KMEA), a new, robust multimarker localization method designed for Autonomous Mobile Robots (AMRs) within Industry 4.0 (I4.0) settings. By integrating the Kabsch Algorithm, our approach significantly enhances localization robustness by aligning detected fiducial markers with their known positions. Unlike conventional methods that rely on a limited subset of visible markers, the KMEA uses all available markers, without requiring the camera's extrinsic parameters, thereby improving robustness. The algorithm was validated in an I4.0 automated warehouse mockup, with a four-stage methodology compared to a previously established marker estimation algorithm for reference. On the one hand, the results have demonstrated the KMEA's similar performance in standard controlled scenarios, with millimetric precision across a set of error metrics and a mean relative error (MRE) of less than 1%. On the other hand, KMEA, when faced with challenging test scenarios with outliers, showed significantly superior performance compared to the baseline algorithm, where it maintained a millimetric to centimetric scale in error metrics, whereas the other suffered extreme degradation. This was emphasized by the average reduced results of error metrics from 86.9% to 92% in Parts III and IV of the test methodology, respectively. These results were achieved using low-cost hardware, indicating the possibility of even greater accuracy with advanced equipment. The paper details the algorithm's development, theoretical framework, comparative advantages over existing methods, discusses the test results, and concludes with comments regarding its potential for industrial and commercial applications by its scalability and reliability.INDEX TERMS ArUco markers, automated warehouse, autonomous mobile robots, fiducial markers, ground truth system, industry 4.0, indoor localization.

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Section: Related Workmentioning

confidence: 99%

Kabsch Marker Estimation Algorithm—A Multi-Robot Marker-Based Localization Algorithm Within the Industry 4.0 Context

Braun,

Lima,

Pereira

et al. 2024

IEEE Access

Self Cite

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Deep Learning-Based Localization Approach for Autonomous Robots in the RobotAtFactory 4.0 Competition

Klein,

Mendes,

Braun

et al. 2024

Communications in Computer and Information Science

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Accurate localization in autonomous robots enables effective decision-making within their operating environment. Various methods have been developed to address this challenge, encompassing traditional techniques, fiducial marker utilization, and machine learning approaches. This work proposes a deep-learning solution employing Convolutional Neural Networks (CNN) to tackle the localization problem, specifically in the context of the RobotAtFactory 4.0 competition. The proposed approach leverages transfer learning from the pre-trained VGG16 model to capitalize on its existing knowledge. To validate the effectiveness of the approach, a simulated scenario was employed. The experimental results demonstrated an error within the millimeter scale and rapid response times in milliseconds. Notably, the presented approach offers several advantages, including a consistent model size regardless of the number of training images utilized and the elimination of the need to know the absolute positions of the fiducial markers.

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Unveiling the Cutting Edge: A Comprehensive Survey of Localization Techniques in WSN, Leveraging Optimization and Machine Learning Approaches

Yadav,

Sharma

2023

Wireless Pers Commun

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A Machine Learning Approach to Robot Localization Using Fiducial Markers in RobotAtFactory 4.0 Competition

Cited by 7 publications

References 42 publications

Kabsch Marker Estimation Algorithm—A Multi-Robot Marker-Based Localization Algorithm Within the Industry 4.0 Context

Kabsch Marker Estimation Algorithm—A Multi-Robot Marker-Based Localization Algorithm Within the Industry 4.0 Context

Deep Learning-Based Localization Approach for Autonomous Robots in the RobotAtFactory 4.0 Competition

Unveiling the Cutting Edge: A Comprehensive Survey of Localization Techniques in WSN, Leveraging Optimization and Machine Learning Approaches

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