Cosi119a

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## Logistics

* People are taking too much advantage of the free extra three days, and then often even asking for an extension on that.
* I feel very generous, but that is causing havoc in our grading.
* So:

<div class="callout callout-big">
        <h1 class="display-5">NEW POLICY</h1>
        <p class="lead">Starting with the Stage 2 Deliverable, work submitted after the due date can never have an exceeds rating</p></div>

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### Standups

* A weekly new deliverable beteween now and the end of the term: <a href="/content/topics/homeworks/hw-active-119/190_hw_119_standups/">Weekly Standups</a>
* The first one will be due next Tuesday

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### Lab Time

1. To the student lab managers, please make sure you have 2 hours per week on the calendar
2. If you have a problem with a robot that you cannot solve, please submit a [Lab Service Request](https://forms.gle/JV7DmjCQcPhkkATx6)

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## Introduction to Fiducials

* A specific kind of graphical image combined with an algorithm
* Generally the recognition algorithm includes a tool to create the images

* Standardized image with known dimensions
* Conceptually simple geometrical transformation
* Perspective distortion is used to compute relative pose (orientation and distance)
* Tricky business with TFs and POV!

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### Aruco Fiducials

* The basic concept of Fiducials can be implemented in different ways
* We use `aruco` fiducials. The other well known one are `apriltags`
* They each have slightly different performance characteristics
* `aruco` fiducials are supported directly by [OpenCV](https://docs.opencv.org/4.x/d5/dae/tutorial_aruco_detection.html)
* We have also used Ubiqutiy Robotics [aruco_detect](http://wiki.ros.org/aruco_detect)

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### The Software

* More generic CV: [OpenCV aruco detection](https://docs.opencv.org/4.x/d5/dae/tutorial_aruco_detection.html) 
* ROS Oriented: [aruco_detect](http://wiki.ros.org/aruco_detect)
* They are both useful depending on what you are doing

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### Characteristics of Aruco Fiducials

* Easily recognized by camera using computer vision
* Known dimensions
* Known pose (location in 6d) within a certain coordinate system
* Encode a number so that your software can tell them apart if there is more than one

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### Creating Aruco Fiducials

* Lots of ways, in code or online
* e.g. [Aruco Markers Generator](https://chev.me/arucogen/)
* You can choose the size, the number of bits, and the number of distinct tags

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### Tag Detection

* How do we get the transform between a camera and fiducials?
* Compare the orientation, size and transfor betwen the obsrved image and the known features of the fiducial
* [ROS Wiki with demo](http://wiki.ros.org/fiducials)

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### Tag Placement

* Trade-offs: along the wall, along the ceiling
* How many tags do you need?
* Can you have too many?
* Remember: the tag is not identifying a `thing`
* It is just a point where your real world coordinate system is bound to your robot coordinate system

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### Fiducial Localization

* In general localization means computing the transform between `real world` and `robot` coordinates.
* More specifically we are computing a transform betwen a `map` tf and a `robot_base` tf
* Even more specifically what is the transform between the `pose` of the fiducial and the `pose` of the camera.

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### Lab Notebook Entries

* A coming homework asks that you write a "howto" for our lab notebook
* Here's one about [Localization with Fiducials](https://campusrover.github.io/labnotebook2/faq/fiducials/fiducial_follows/?h=fiducials) from another semester

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### Other Resources
* [fiducial_follow](https://github.com/UbiquityRobotics/demos/tree/master/fiducial_follow)
* [Ubiquity Robotics Fiducials](https://learn.ubiquityrobotics.com/fiducials)

* Two types of Fiducial Localization

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### Fiducial SLAM

* Detect fideucials in image
* Compute transform between fiducial(s) and camera (and robot)
* Orient a coordinate system corresponding to the real world

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### Fiducial Localization with an existing map

* Predefine a map with fiducials marked on it
* Detect fiducials in camera image
* Find transforms between fiducials and camera
* Localize robot on that map

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<h2 class="shadow p-3 bg-white rounded">Thank you. Questions?<img class="img-fluid w-100" src="https://picsum.photos/800/100.jpg" /> <small> (random Image from picsum.photos)</small></h2>