SLAM - The Primary Technology Behind AR

If SLAM is a new term to you and you want to know more about it, you are on the right page. SLAM is a new technology that is employed to enable a mobile robot for vehicles to detect the surrounding environment. The idea is to spot its position on the map. Primarily, this technology is associated with robotics, but it can also be employed in a lot of other devices and machines, such as drones, automatic aerial vehicles, automatic forklifts, and robot cleaners just to name a few. Let's get a deeper insight into this technology.

The Advent of SLAM

In 1995, SLAM was introduced for the first time at the International Symposium on Robotics Research. In 1986, a mathematical definition was presented at the IEEE Robotics and Automation Conference. After the conference, studies were carried out in order to find more about the navigation devices and statistical theories.

After more than a decade, experts introduced a method to implement one camera to achieve the same goal instead of using multiple sensors. As a result, these efforts led to the creation of vision-based SLAM. This system used cameras in order to get a three-dimensional position.

Without any doubt, this was a great achievement of that era. Since then, we have seen the application of these systems in a number of areas.

The Core, Mapping, and localization of SLAM

Now, let's find out more about mapping, localization, and the core of SLAM systems. This will help you find out more about this technology and have a better understanding of how it is proven beneficial.

Localization

Localization can help you figure out where you are. Basically, SLAM gives you an estimation of the location on the basis of visual information. It is like when you come across a weird place for the first time.

Since we humans do not have a clear sense of defense and distance, we may get lost. The great thing about SLAM-based robots is that they can easily figure out the direction with respect to the surrounding environment. However, it is important that the map should be highly trained in order to spot your location.

Mapping

Mapping refers to a process that helps analyze information collected by the robot through a sensor. Generally, vision-based systems make use of cameras as sensitive sensors. After the creation of enough motion parallax, amidst two-dimensional locations, triangulation techniques are deployed to get a three-dimensional location.

The beauty of augmented reality is that it can help obtain information from virtual images in a real environment. However, augmented reality requires certain technologies in order to recognize the environment around it and spot the relative position of cameras.

So, you can see that SLAM plays a very important role in a number of areas like location interaction, interface, graphics, display, and tracking.

Long story short, this was an introduction to the technology behind SLAM and various areas where it is implemented.

If you want to get a deeper insight into Simultaneous Localization and Mapping, you can browse simultaneous localization and mapping SLAM AI.

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What Is Simultaneous Localization and Mapping?

Robots use maps in order to get around just like humans. As a matter of fact, robots cannot depend on GPS during their indoor operation. Apart from this, GPS is not accurate enough during their outdoor operation due to increased demand for decision. This is the reason these devices depend on Simultaneous Localization and Mapping. It is also known as SLAM. Let's find out more about this approach.

With the help of SLAM, it is possible for robots to construct these maps while operating. Besides, it enables these machines to spot their position through the alignment of the sensor data.

Although it looks quite simple, the process involves a lot of stages. The robots have to process sensor data with the help of a lot of algorithms.

Sensor Data Alignment

Computers detect the position of a robot in the form of a timestamp dot on the timeline of the map. As a matter of fact, robots continue to gather sensor data to know more about their surroundings. You will be surprised to know that they capture images at a rate of 90 images per second. This is how they offer precision.

Motion Estimation

Apart from this, wheel odometry considers the rotation of the wheels of the robot to measure the distance traveled. Similarly, inertial measurement units can help computer gauge speed. These sensor streams are used in order to get a better estimate of the movement of the robot.

Sensor Data Registration

Sensor data registration happens between a map and a measurement. For example, with the help of the NVIDIA Isaac SDK, experts can use a robot for the purpose of map matching. There is an algorithm in the SDK called HGMM, which is short for Hierarchical Gaussian Mixture Model. This algorithm is used to align a pair of point clouds.

Basically, Bayesian filters are used to mathematically solve the location of a robot. It is done with the help of motion estimates and a stream of sensor data.

GPUs and Split-Second Calculations

The interesting thing is that mapping calculations are done up to 100 times per second based on the algorithms. And this is only possible in real-time with the astonishing processing power of GPUs. Unlike CPUs, GPUs can be up to 20 times faster as far as these calculations are concerned.

Visual Odometry and Localization

Visual Odometry can be an ideal choice to spot the location of a robot and orientation. In this case, the only input is video. Nvidia Isaac is an ideal choice for this as it is compatible with stereo visual odometry, which involves two cameras. These cameras work in real-time in order to spot the location. These cameras can record up to 30 frames per second.

Long story short, this was a brief look at Simultaneous Localization and Mapping. Hopefully, this article will help you get a better understanding of this technology.

Are you looking for more information about simultaneous localization and mapping (SLAM) patent? If so, we suggest that you check out patent on obstacle RECOGNITION and SLAM.

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