Skip to main content

How to create AR Glasses

In this blog, I will talk about how to create AR Glasses using the current technologies available.

This is about creating a practical pair of glasses one can wear daily.

I have three versions.

- Basic AR Glasses
- Sports AR Glasses
- Ultimate AR Glasses


Basic AR Glasses

What is:
- A practical pair of glasses one can wear daily.
- Wirelessly connected to a computing platform.
- This computing platform can be a standalone platform.

Computing Platform
- If we create our AR Glasses to use iPhone or Android phone, we don't have to ask our users to carry a separate computing platform.
- But we will be dependent on Apple, Google or Samsung eco system.
- This can be a stand alone platform.
- If it is a stand alone platform, it should be able to charge AR Glasses directly from it, wired or wirelessly.
- If it is a standalone platform, it should be a smart phone customized for AR Glasses.
- If we plan to make a non-smartphone platform to replace a smartphone, sorry to say we don't have the technology advancements in the next 15 years to make this happen.
- So, as a Trojan hours, its beginning should be dependent upon the user's existing smartphone.
- Or. replace the user's smartphone with a new type of a smartphone that beats the current smartphone Apple or Google.


Sports AR Glasses

What is:
- A practical pair of glasses one can wear in sports activity.
- eg) Hiking, Running, Swimming, Skiing, Surfing, etc.
- Water-Proof
- Dirt-Proof
- Battery should last an hour or two.

Ultimate AR Glasses

What is:
- A practical pair of glasses one can wear in all activities
- May meet the military standards and requirements
- Only this pair will deliver the content not possible in all types of AR Glasses above.
- Hint:
-- This will use the advancements in AI, Computer Vision, Physics and Optics.
-- No, I'm not taking about WaveGuides Display.  That's 1980's military technology.
-- There are some advancements in these technologies which will be better than what others can create in the next 20 years.


Pricing
- Basic AR Glasses: under $250
- Sports AR Glasses: under $350
- Ultimate AR Glasses: under $550

Content
- I know what type of the contents to have to make these AR Glasses successful products. 
- I won't disclose them here, but you can contact me for more info.
- No, I'm not taking about displaying the navigation, weather, health info, etc.
- That doesn't make our customers wants to buy our AR Glasses.





Comments

Anonymous said…
This comment has been removed by a blog administrator.

Popular posts from this blog

How to project a camera plane A to a camera plane B

How to Create a holographic display and camcorder In the last part of the series "How to Create a Holographic Display and Camcorder", I talked about what the interest points, descriptors, and features to find the same object in two photos. In this part of the series, I'll talk about how to extract the depth of the object in two photos by calculating the disparity between the photos. In order to that, we need to construct a triangle mesh between correspondences. To construct a mesh, we will use Delaunnay triagulation.  Delaunnay Triagulation - It minimizes angles of all triangles, while the sigma of triangles is maximized. The reason for the triangulation is to do a piece wise affine transformation for each triangle mapped from a projective plane A to a projective plane B. A projective plane A is of a camera projective view at time t, while a projective plane B is of a camera projective view at time t+1. (or, at t-1.  It really doesn't matter)...

State of the Art SLAM techniques

Best Stereo SLAMs in 2017 are reviewed. Namely, (in arbitrary order) EKF-SLAM based,  Keyframe based,  Joint BA optimization based,  RSLAM,  S-PTAM,  LSD-SLAM,   Best RGB-D SLAMs in 2017 are also reviewed. KinectFusion,  Kintinuouns,  DVO-SLAM,  ElasticFusion,  RGB-D SLAM,   See my keypoints of the best Stereo SLAMs. Stereo SLAM Conditionally Independent Divide and Conquer EKF-SLAM [5]   operate in large environments than other approaches at that time uses both  close and far points far points whose depth cannot be reliably estimated due to little disparity in the stereo camera  uses an inverse depth parametrization [6] shows empirically points can be triangulated reliably, if their depth is less than about 40 times the stereo baseline.     - Keyframe-based  Stereo SLAM   - uses BA optimization in a local area to archive scalability.  ...

How to train a neural network to retrieve 3D maps from videos

This blog is about how to train a neural network to extract depth maps from videos of moving people captured with a monocular camera. Note: With a monocular camera, extracting the depth map of moving people is difficult.  Difficulty is due to the motion blur and the rolling shutter of an image.  However, we can overcome these limitations by predicting the depth maps by the model trained with a generated dataset using SfM and MVS from the normalized videos. This normalized dataset can be the basis of the training set for the neural network to automatically extract the accurate depth maps from a typical video footage, without any further assistance from a MVS. To start this project with a SfM and a MVS, we will use TUM Dataset. So, the basic idea is to use SfM and Multiview Stereo to estimate depth, while serves as supervision during training. The RGB-D SLAM reference implementation from these papers are used: - RGB-D Slam (Robotics OS) - Real-time 3D Visual SLAM ...