Basics

Often abbreviated as AR
A "layering" of digital data similar to that used in VR or MR, but layered directly over real life
Thus a semi-transparent "screen" is used
Can use inside out, or outside in tracking, although inside out is most common due to the highly mobile nature of most AR applications
Most common tracking methods are
- Marker Based Inside Out Tracking
- Computer Vision Based Inside Out Tracking
- SteamVR Tracking
- Optitrack
- Accelorometer-Compass-Gyroscope Based Dead Reconing
The core displays are easy (phone, phone and Google Cardboard type headset, dedicated AR HMD)
The main challenges are:
- Tracking
- Software (This is changing with OpenXR though..._
- Social Acceptance (especially of bulky, goofy looking headsets being "what works", and fashionable headsets like Google Glass (which isn't TECHNICALLY ar...), and Magic Leap , being near useless )

Main OSE Use Cases

Can use a headset or some sort of handheld display
Uses Object Recognition to see where the "parts" and "tools" are, and then calculate + show what needs to be done in the current "step"
May be hardware intensive, but is doable, especially with [AR/MR/VR Display Tethering]]

Open Source AR CAD Software
Essentially fiddling around with your hands, but it goes to cad
May allow for multiple people, if using a very connected work enviroment, or a LARGE pc/server capable of many virtual clients

Project North Star is VERY capable hardware wise
- Still needs software, but this is developing
The main issue is the need for a 3D Printer, Maker Experience, And part sourcing
- ALL OF WHICH a microfactory/makerspace etc could solve
Thus this could become a small scale enterprise (or larger if long distance online sales are included in the business plan)\

FLOSS using https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/ - Example using simple markers (ArUco) markers - with Python. When you see an icon, app replaces image with another image to augment information of image. OSE Use Case: building a 3D printer, aruco marker is attached to a part, and a video tells you how to build that part. This way, just with an app and marked parts - you can build an entire thing with 'self-generated' instructions. The savings here come from not needing to identify how a part goes together by looking at documentation. This requires you to (1) find and identify part; (2) follow instructions on that part. Challenges: identifying a part from many parts can be tricky if you have to dig through a bunch of parts. Following instructions can be cumbersome. Solutions with AR: part is identified automatically (pending marker). Quick on-demand, repeating instructions can be shown automatically, without you going through pages or hitting play for a video.
- Overall SWOT: good to identify parts, but you still have to put on the labels. If labels are done automatically - such as by image recognition, not marker - then we are set. Threat: cumbersome to learn unless there is a clear instructional. Also, small parts such as small screws - it's not easy to label them. Conclusion: Image Recognition + AR is the solution. *Image Recognition*