What Is Simultaneous Mapping And Localization?

 Robots depend on maps to move around. Although they can use GPS, it is not enough when they are operating indoors. Another problem with GPS is that it is not accurate enough. Therefore, robots cannot depend on GPS. Therefore, these machines depend on Simultaneous Localization and Mapping, which is abbreviated to SLAM. Let's find out more about this technology.

With the help of SLAM, different types of machines such as robots create maps as they move around. With these maps, they move around without crashing into different objects in a room. It may sound simple, but this process consists of multiple stages that involve sensor data alignment with the help of a number of algorithms. These algorithms use the power of the GPUs of today.

Sensor Data Alignment

Today's computers consider the position of a robot as a timestamp dot on a timeline or a map. Besides, robots continue to collect data about their surroundings using these sensors. The interesting part is that camera images are captured 90 times per second for proper measurements. When robots move around, data points make it easier for the robot to prevent accidents.

Motion Estimation

Besides, wheel odometry considers the rotation of the wheels of the robot. The purpose is to help the robot measure its travel distance. Apart from this, they also use the inertial measurement units to estimate acceleration and speed.

Sensor Data Registration

Since data registration is done between two measurements on a map. Expert developers can easily localize a robot using scan-to-map matching.

GPUs that perform Split-Second Calculations

The speed of these mapping calculations is between 20 and 100 times per second. It all depends upon the algorithms. And the good thing is that these robots use powerful GPUs in order to perform these calculations.

Unlike a regular CPU, a powerful GPU is up to 20 times faster. Therefore, simultaneous localization and mapping use powerful graphics processing units.