We have made great strides when it comes to robotics. But where we’ve gotten stuck is the lack of support for robots in finding the location.

WHAT IS SLAM?

However, Computer Vision has also found a solution for this. Simultaneous locating and mapping are here for robots to guide you every step of the way, just like a GPS.

While GPS serves as a good mapping system, certain restrictions limit its range. For example, the interiors restrict its reach and the exteriors have various barriers that, if the robot hits, can jeopardize its safety.

And so our safety vest is simultaneous location and mapping, better known as SLAM, which helps you find locations and map your trips.

HOW DOES SLAM WORK?

Since robots can have large memory banks, they keep mapping their location with the help of SLAM technology. So when recording your trips, you draw maps. This is very useful when the robot has to chart a similar course in the future.

Also, with GPS, certainty regarding the robot’s position is not a guarantee. But SLAM helps determine position. It uses multi-level alignment of sensor data to do so, in the same way, it creates a map.

Now while this lineup seems easy enough, it isn’t. The alignment of sensor data as a process has many levels. This multifaceted process requires the application of several algorithms. And for that, we need supreme computing vision and supreme processors found in GPUs.

SLAM AND ITS WORKING MECHANISM

When you have a problem, SLAM (simultaneous localization and mapping) solves it. The solution is what helps robots and other robotic units like drones and wheeled robots etc. find your way out or into a particular space. This is useful when the robot cannot use GPS or a built-in map or any other reference.

Calculate and determine the way to go with respect to the position and orientation of the robot with respect to various objects in the vicinity.

SENSORS AND DATA

Use sensors for this purpose. The different sensors through cameras (using LIDAR and accelerator meter and an inertial measurement unit) collect data. This consolidated data is then broken down to create maps.

The sensors have helped to increase the degree of precision and robustness of the robot. Prepare the robot even in adverse conditions.

TECHNOLOGY USED

The cameras take 90 images in one second. It does not end here. In addition, the cameras also click 20 LIDAR images in one second. This gives an accurate and accurate account of the nearby surroundings.

These images are used to access data points to determine the location relative to the camera and then plot the map accordingly.

Also, these calculations require fast processing that is only available on GPUs. About 20-100 calculations are performed in the span of one second.

To conclude, he collects data by evaluating spatial proximity and then uses algorithms to decipher these juxtapositions. Finally, the robot creates a map.