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Some of you know I have been recently experimenting a bit more with WebXR than a WebVR and when we talk about mobile Mixed Reality, ARkit and ARCore is something which plays a pivotal role to map and understand the environment inside our applications.





I am planning to write a series of blog posts on how you can start developing WebXR applications now and play with them starting with the basics and then going on to using different features of it. But before that, I planned to pen down this series of how actually the "world mapping" works in arcore and arkit. So that we have a better understanding of the Mixed Reality capabilities of the devices we will be working with.





Mapping: feature detection and anchors

Creating apps that work seamlessly with arcore/kit requires a little bit of knowledge about the algorithms that work in the back and that involves knowing about Anchors.

What are anchors:

Anchors are your virtual markers in the real world. As a developer, you anchor any virtual object to the real world and that 3d model will stay glued into the physical location of the real world. Anchors also get updated over time depending on the new information that the system learns. For example, if you anchor a pikcahu 2 ft away from you and then you actually walk towards your pikachu, it realises the actual distance is 2.1ft, then it will compensate that. In real life we have a static coordinate system where every object has it's own x,y,z coordinates. Anchors in devices like HoloLens override the rotation and position of the transform component.

How Anchors stick?

If we follow the d ocumentation in Google ARCore then we see it is attached to something called "trackables", which are feature points and planes in an image. Planes are essentially clustered feature points. You can have a more in-depth look at what Google says ARCore anchor does by reading their really nice Fundamentals . But for our purposes, we first need to understand what exactly are these Feature Points.

Feature Points: through the eyes of computer vision

Feature points are distinctive markers on an image that an algorithm can use to track specific things in that image. Normally any distinctive pattern, such as T-junctions, corners are a good candidate. They lone are not too useful to distinguish between each other and reliable place marker on an image so the neighbouring pixels of that image are also analyzed and saved as a descriptor.

Now a good anchor should have reliable feature points attached to it. The algorithm must be able to find the same physical space under different viewpoints. It should be able to accommodate the change in