Robots use maps to move like humans. In fact, robots cannot rely on GPS during indoor operation. Apart from that, GPS is not accurate enough during outdoor operation due to the growing demand for decision-making. This is why these devices depend on simultaneous localization and mapping. It is also known as SLAM. Let’s learn more about this approach.
With the help of SLAM, robots can build these maps during operation. In addition, it allows these machines to detect their position through the alignment of sensor data.
Although it seems quite simple, the process involves many stages. Robots need to process sensor data with the help of many algorithms.
Alignment of sensor data
Computers detect the location of a robot in the form of a time point in the map timeline. In fact, robots continue to collect sensor data to learn more about their environment. You will be surprised to learn that they capture images at a rate of 90 images per second. That’s how they offer precision.
Estimate of movement
Apart from that, the wheel odomemetry considers the rotation of the robot’s wheels to measure the distance traveled. Similarly, inertial units of measurement can help the speed of the computer meter. These sensor currents are used to get a better estimate of the robot’s movement.
Recording sensor data
Sensor data is recorded between a map and a measurement. For example, with the help of NVIDIA Isaac SDK, experts can use a robot to match maps. 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 flow of sensor data.
GPUs and two-second calculations
The interesting thing is that mapping calculations are performed up to 100 times per second depending on the algorithms. And this is only possible in real time with the incredible processing power of GPUs. Unlike CPUs, GPUs can be up to 20 times faster when it comes to these calculations.
Visual odomemetry and position
Visual Odometry can be the ideal choice to detect the location and orientation of a robot. In this case, the only input is the video. Nvidia Isaac is the ideal choice for this as it supports stereo visual odomemetry, which involves two cameras. These cameras work in real time to detect location. These cameras can record up to 30 frames per second.
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