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.
May 14, 2009 at 10:50:00 PM PDT

Post a Comment

Popular posts from this blog

Time of Flight Depth Sensor (ToF) - Pros and Cons

Pros - Lightweight - Full frame time-of-flight data (3D array) collected with a single laser pulse - Unambiguous direct calculation of range - Blur-free imager without motion distortion - Co-registeration of range and intensity for each pixel - Perfectly registered pixels within a frame - Ability to represent the camera-oblique objects - No precision scanning mechanism required - 3D flash LIDAR with 2D cameras (EO and IR) to combine 2D texture over 3D depth - Multiple 3D flash LIDAR cameras for full volumetric 3D scene - Lighter and smaller than point scanning systems - Non-moving parts - Lower power consumption - Ability to scan through range-gating, natural obscurants
Post a Comment
Read more

How to improve the traditional ASR using Connectionist Temporal Classification

The traditional Automatic Speech Recognition (ASR) performs at about 85% accuracy rate.  At this rate, ASR users are often frustrated with the experience with using such a system. The tradition ASR is often fragile: 1) requires extensive modification of parameters, just to make it work. 2) requires extensive understanding of a language model and a acoustic model. 3) doesn't scale well to multiple languages. 4) hyper-sensitive to speaker variants. Deep Learning on the acoustic model has been introduced, but not much of gain in the accuracy. What if, we can do a DL from end to end? Connectionist Temporal Classification (2006) introduces an idea of using FFT on the frequency of a recording of a voice command and constructs a spectrogram at 8kHz.  At each spectrogram interval, a DL neural network can be assigned, individually. The basic idea is to have RNN output neurons to encode distribution over "symbols". The traditional ASR uses a phone...
Post a Comment
Read more

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.  ...
Post a Comment
Read more