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.

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