Automatic generation and detection of highly reliable fiducial markers under occlusion

Garrido-Jurado, Sergio; Muñoz-Salinas, Rafael; Madrid-Cuevas, F. J.; Marín-Jiménez, Manuel J.

doi:10.1016/j.patcog.2014.01.005

Cited by 1,992 publications

(1,021 citation statements)

References 34 publications

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“…1) consists of both pose estimation relative material and hardware for generation of virtual content. There are seven 2D ArUco barcode markers (Garrido-Jurado et al, 2014) attached inside the tractor cabin and they are used for the head localization process. Furthermore, the selected machine vision camera (DFK 41AU02.AS from The Imaging Source) gives 15 frames per second in the Bayer8 format.…”

Section: System Setupmentioning

confidence: 99%

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Augmented Reality in Forest Machine Cabin

Palonen¹,

Hyyti

Visala³

2017

IFAC-PapersOnLine

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Augmented reality human machine interface is demonstrated in the cabin of a forest machine outdoors for the first time in real time. In this work, we propose a system setup and a real-time capable algorithm to augment the operator's visual field with measurements from the forest machine and its environment. In the demonstration, an instrumented forestry crane and a lidar are used to model the pose of the crane and its surroundings. In our approach, a camera and an inertial measurement unit are used to estimate the pose of the operator's head in difficult lighting conditions with the help of planar markers placed on the cabin structures. Using the estimate, a point cloud and a crane model are superimposed on the video feed to form an augmented reality view. Our system is tested to work outdoors using a forest machine research platform in real time with encouraging initial results.

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Section: System Setupmentioning

confidence: 99%

“…For pose estimation, we are using methods from the ArUco library (Garrido-Jurado et al, 2014) with 2D barcode markers, detected by the camera, distributed inside the cabin. There are also similar AR libraries available like ArToolkit (Kato, H. and Billingurst, M., 1999) and ALVAR (VTT, 2009).…”

Section: Introductionmentioning

confidence: 99%

Augmented Reality in Forest Machine Cabin

Palonen¹,

Hyyti

Visala³

2017

IFAC-PapersOnLine

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“…However, identifying such features and reconstructing their position and orientation in 3D space is a challenging computer vision problem. A common solution is to use planar fiducial markers of known geometry (Kato and Billinghurst, 1999;Garrido-Jurado et al, 2014) (Figure 4D). The computer vision research community has developed some open-source software solutions to this problem (Garrido-Jurado et al, 2014), which have been integrated into Bonsai to allow the possibility of easily and flexibly incorporating online 3D fiducial tracking in video streams.…”

Section: Applicationsmentioning

confidence: 99%

“…A common solution is to use planar fiducial markers of known geometry (Kato and Billinghurst, 1999;Garrido-Jurado et al, 2014) (Figure 4D). The computer vision research community has developed some open-source software solutions to this problem (Garrido-Jurado et al, 2014), which have been integrated into Bonsai to allow the possibility of easily and flexibly incorporating online 3D fiducial tracking in video streams. This approach has been used successfully to record 3D head movements of a mouse under optogenetic stimulation in a decisionmaking task ( Figure 4D).…”

Section: Applicationsmentioning

confidence: 99%

“…A fiber optic cable was attached to the mouse's head. The 3D position and orientation of the head was tracked in real-time using square fiducial markers from the ArUco tracking library (Garrido-Jurado et al, 2014). The video (0.24 Megapixel) was simultaneously compressed to a high-quality H.264 encoded file that was used for subsequent offline analysis of the behavior.…”

Section: D Tracking Of Rodent Head Positionmentioning

confidence: 99%

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Bonsai: An event-based framework for processing and controlling data streams

Lopes

Bonacchi

Frazão

et al. 2014

Preprint

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The design of modern scientific experiments requires the control and monitoring of many different data streams. However, the serial execution of programming instructions in a computer makes it a challenge to develop software that can deal with the asynchronous, parallel nature of scientific data. Here we present Bonsai, a modular, high-performance, open-source visual programming framework for the acquisition and online processing of data streams. We describe Bonsai's core principles and architecture and demonstrate how it allows for the rapid and flexible prototyping of integrated experimental designs in neuroscience. We specifically highlight some applications that require the combination of many different hardware and software components, including video tracking of behavior, electrophysiology and closed-loop control of stimulation.

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Application of AR Technology in Bioengineering

Nikolić

Filipović

2021

Computational Modeling and Simulation Examples in Bioengineering

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Automatic generation and detection of highly reliable fiducial markers under occlusion

Cited by 1,992 publications

References 34 publications

Augmented Reality in Forest Machine Cabin

Augmented Reality in Forest Machine Cabin

Bonsai: An event-based framework for processing and controlling data streams

Application of AR Technology in Bioengineering

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