Augmented Reality: Difference between revisions
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=Basics= | =Basics= | ||
*Often abbreviated as '''AR''' | |||
*A "layering" of digital data similar to that used in [[VR]] or [[MR]], but layered directly over real life | *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 | *Thus a semi-transparent "screen" is used | ||
Revision as of 20:16, 7 April 2020
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
Use Case for Build Instructionals using Markers
- 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*
Links
- OS AR based on markers - https://www.openspace3d.com/softwarelogiciel/