Improving Self-Localization Using CNN-based Monocular Landmark Detection and Distance Estimation in Virtual Testbeds

Atanasyan, Alexander; Roßmann, Jürgen

doi:10.1007/978-3-662-59317-2_25

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…In recent years, machine learning (ML) techniques [26][27][28][29] have also been applied to indoor localization to improve the accuracy and adaptability of the systems.…”

Section: Related Workmentioning

confidence: 99%

A Performance Comparison between Different Industrial Real-Time Indoor Localization Systems for Mobile Platforms

Rebelo,

Lima,

Soares

et al. 2024

Sensors

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The flexibility and versatility associated with autonomous mobile robots (AMR) have facilitated their integration into different types of industries and tasks. However, as the main objective of their implementation on the factory floor is to optimize processes and, consequently, the time associated with them, it is necessary to take into account the environment and congestion to which they are subjected. Localization, on the shop floor and in real time, is an important requirement to optimize the AMRs’ trajectory management, thus avoiding livelocks and deadlocks during their movements in partnership with manual forklift operators and logistic trains. Threeof the most commonly used localization techniques in indoor environments (time of flight, angle of arrival, and time difference of arrival), as well as two of the most commonly used indoor localization methods in the industry (ultra-wideband, and ultrasound), are presented and compared in this paper. Furthermore, it identifies and compares three industrial indoor localization solutions: Qorvo, Eliko Kio, and Marvelmind, implemented in an industrial mobile platform, which is the main contribution of this paper. These solutions can be applied to both AMRs and other mobile platforms, such as forklifts and logistic trains. In terms of results, the Marvelmind system, which uses an ultrasound method, was the best solution.

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“…In recent years, machine learning (ML) techniques [26][27][28][29] have also been applied to indoor localization to improve the accuracy and adaptability of the systems.…”

Section: Related Workmentioning

confidence: 99%

A Performance Comparison between Different Industrial Real-Time Indoor Localization Systems for Mobile Platforms

Rebelo,

Lima,

Soares

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Recently, with the advancement of AI, also some AI-based algorithms have been developed. For instance, the Convolutional Neural Network (CNN) was used to help locate the robot using images from a robot camera and other sensors [ 23 , 24 ]. Interesting related work is presented in [ 25 ], where the authors used images for localization in a scenario with 6-DoF (Degrees of Freedom).…”

Section: State Of the Artmentioning

confidence: 99%

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

Klein

Braun

Mendes

et al. 2023

Sensors

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Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.

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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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Improving Self-Localization Using CNN-based Monocular Landmark Detection and Distance Estimation in Virtual Testbeds

Cited by 3 publications

References 10 publications

A Performance Comparison between Different Industrial Real-Time Indoor Localization Systems for Mobile Platforms

A Performance Comparison between Different Industrial Real-Time Indoor Localization Systems for Mobile Platforms

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

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

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