docs/user_guide/aruco_markers_pipeline/introduction.md


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Overview
========

This section explains how to build augmented reality pipelines based on ArUco Markers technology for various use cases.  

The OpenCV library provides a module to detect fiducial markers into a picture and estimate their poses (cf [OpenCV ArUco tutorial page](https://docs.opencv.org/4.x/d5/dae/tutorial_aruco_detection.html)).

![OpenCV ArUco markers](https://pyimagesearch.com/wp-content/uploads/2020/12/aruco_generate_tags_header.png)

The ArGaze [ArUcoMarkers submodule](../../argaze.md/#argaze.ArUcoMarkers) eases markers creation, optic calibration, markers detection and 3D scene pose estimation through a set of high level classes.

First, let's look at the schema below: it gives an overview of the main notions involved in the following chapters.

![ArUco markers pipeline](../../img/aruco_markers_pipeline.png)

To build your own ArUco markers pipeline, you need to know:

* [How to setup ArUco markers into a scene](aruco_markers_description.md),
* [How to describe areas of interest in the same scene](aoi_description.md),
* [How to deal with an ArUcoCamera instance](aruco_camera_configuration_and_execution.md),
* [How to add ArUcoScene instance](aruco_scene.md),
* [How to visualize ArUcoCamera and ArUcoScenes](visualisation.md)

More advanced features are also explained like:

* [How to script ArUco markers pipeline](advanced_topics/scripting.md)
* [How to calibrate optic parameters](optic_parameters_calibration.md)
* [How to improve ArUco markers detection](advanced_topics/aruco_detector_configuration.md)