The robot relies on the map to move. GPS is available, but not enough when operating indoors. Another problem with GPS is that it is not accurate. So the robot cannot rely on GPS. Thus, these machines rely on concurrent localization and mapping, abbreviated as SLAM. Let’s take a closer look at this technique.
With the help of SLAM, various types of machines, such as robots, move and create maps. Use this map to move around without colliding with other objects in the room. As simple as it may sound, this process consists of several steps that involve sorting sensor data with the help of several algorithms. These algorithms use the power of today’s GPUs.
Sort sensor data
Today’s computers treat a robot’s location as a timestamp point on a timeline or map. Robots also use these sensors to continuously collect data about their surroundings. The interesting part is that the camera images are captured 90 times per second for proper measurements. As the robot moves, data points make it easier for the robot to prevent accidents.
motion estimation
Wheel odometer measurements also take into account the rotation of the robot’s wheels. The purpose is to help the robot measure the distance traveled. In addition to this, inertial units of measure are used to estimate acceleration and velocity.
Register sensor data
This is because data registration takes place between two measurements on the map. Professional developers can easily localize robots using scan-map matching.
GPU performing split-second calculations
The rate of these mapping calculations is 20-100 times per second. It all depends on the algorithm. And the good thing is that this robot uses a powerful GPU to perform these calculations.
Unlike regular CPUs, powerful GPUs are up to 20x faster. Therefore, simultaneous localization and mapping use a powerful graphics processing unit.
Visual mileage measurement to aid localization
The purpose of visual odometer measurement is to recover the robot’s orientation and position. The powerful GPU uses two cameras that operate in real time to guide you at a rate of 30 frames per second.
With the help of stereo visual odometer measurements, robot developers can determine the position of the robot and use it for appropriate navigation. Besides, future advancements in the world of visual odometer measurement could help make it easier than ever before.
Create maps to help with localization
There are three ways to create a map. In the first method, the mapping algorithm works under the supervision of a supervisor. So the process is controlled manually. The second method, on the other hand, requires the power of the workstation to do this.
In the third method, odometer data and record rider scans can help make the job easier. This approach can help log mapping applications to do the mapping offline.
In a nutshell, I hope this article will help you gain a better understanding of concurrent localization and mapping.
