How to change augmented reality through SLAM technology application

Augmented reality has become a hotter technology than VR technology, and its prosperity is still behind. Recently, we will introduce how to use the application of SLAM technology to change the augmented reality technology.

When these two different ways of interaction begin to blend seamlessly, I believe we will see the most powerful and widely adopted augmented reality implementation.

Despite the limited penetration rate, augmented reality is clearly one of the most popular technology areas. This is largely due to the traditional way of implementing AR through tag tracking and the attendant high threshold barriers.

In addition to 3D models and animations, the fulcrum of augmented reality is based on two closely related technologies: image recognition and markup. When paired together, they can support the camera to identify the image's data, triggering the associated experience and tracking the eye position relative to the camera in three dimensions. By uploading images to the processing server, developers can associate one or more images to a single augmented reality experience. In this way, the camera on the device can understand and track the superimposed digital content.

For how it works, we first understand two core concepts. The first is Marker DetecTIon, which recognizes an image (often called a marker) through a camera lens and associates with a corresponding part of the server to trigger the experience; secondly, Marker Tracking, or the ability to maintain physical objects Or mark the real-time direction and constantly update the digital content to simulate its capabilities.

An example of a tag-based AR experience

The quality of detection and tracking is determined by a series of parameters, mainly the complexity of the comparison points and the repetitive patterns that the markers lack. For the complex and difficult points of excellent markup and bad clicks, I can start another article to explain. Although this still provides a powerful experience for users, it has obvious limitations, and its dependence has been plaguing the entire industry. From a user experience point of view, I not only need to download a specific application, but also need the corresponding physical object to activate the experience. But with SLAM, users can access content with just one mobile device.

The English abbreviation for instant location and map construction is SLAM. This technology has only recently become a viable option, as more and more devices are starting to carry auxiliary depth cameras (at the time of this writing, Google and Apple have found ways to bypass dedicated auxiliary cameras) And this is a necessary condition to support SLAM.

This technology basically proactively identifies walls, floors and other physical obstacles in space. Currently, most applications that use SLAM simply use floor recognition and position tracking to place AR objects on the surface around the user. A few platforms can handle additional spatial information (such as walls, ceilings, furniture, etc.) to gain a deeper understanding of the surrounding environment. The two platforms that currently make extensive use of these features are Apple ARKit and Google ARCore.

This provides diversity and flexibility for developers to create AR experiences that were previously impossible. SLAM is able to proactively estimate the surface it believes to exist hundreds of times per second and fix these microscopic positions with traces or vertices. By creating a dense point cloud reconstruction, the device camera not only recognizes the physical space, but also remembers the relative position of the object as it turns. The following is a demonstration of the technology when processing information in real time:

A tracking-based experience can provide a huge advantage: a scenario. When a user picks up an object, the activated digital content feels like it is part of the object. If I activate an AR experience from the whole wheat honey nut, then the cartoon mascot flies away from the box, which will be a meaningful implementation. This behavior allows an application to truly become a "browser" in the physical world. It is much more difficult to do this with SLAM, because although the computer can better understand the form of the world, it cannot provide substantial feedback based on what it sees.

With SLAM, users can realistically control cartoon plumbers (such as Mario) in the office to collect gold coins or avoid dangerous carnivorous plants; renovate the bedroom with furniture and posters that do not actually exist; or place a street on the street Series arrows, then use GPS to locate the nearest coffee shop. When these two different ways of interaction begin to blend seamlessly, I believe we will see the most powerful and widely adopted augmented reality implementation. The potential of this fusion technology is endless.

The application of SLAM technology is not limited to augmented reality. Overall, SLAM can help provide computers with a more literal "eye" that allows them to visually understand everything around them. This understanding has been extended to robotics, autonomous vehicles, machine learning and artificial intelligence.

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