simultaneous localization and mapping course

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simultaneous localization and mapping course

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.

Basically, Bayesian filters are used to mathematically solve the location of a robot. It is done with the help of motion estimates and a stream of sensor data.

GPUs and Split-Second Calculations

The interesting thing is that mapping calculations are done up to 100 times per second based on the algorithms. And this is only possible in real-time with the astonishing processing power of GPUs. Unlike CPUs, GPUs can be up to 20 times faster as far as these calculations are concerned.

Visual Odometry and Localization

Visual Odometry can be an ideal choice to spot the location of a robot and orientation. In this case, the only input is video. Nvidia Isaac is an ideal choice for this as it is compatible with stereo visual odometry, which involves two cameras. These cameras work in real-time in order to spot the location. These cameras can record up to 30 frames per second.

Long story short, this was a brief look at Simultaneous Localization and Mapping. Hopefully, this article will help you get a better understanding of this technology.


Simultaneous localization and mapping

SLAM will always use several different types of sensors, and the powers and limits of various sensor types have been a major driver of new algorithms.

Statistical independence is the mandatory requirement to cope with metric bias and with noise in measurements. Different types of sensors give rise to different SLAM algorithms whose assumptions are most appropriate to the sensors.

At one extreme, laser scans or visual features provide details of many points within an area, sometimes rendering SLAM inference is unnecessary because shapes in these point clouds can be easily and unambiguously aligned at each step via image registration. At the opposite extreme, tactile sensors are extremely sparse as they contain only information about points very close to the agent, so they require strong prior models to compensate in purely tactile SLAM. Most practical SLAM tasks fall somewhere between these visual and tactile extremes.

Sensor models divide broadly into landmark-based and raw-data approaches. Landmarks are uniquely identifiable objects in the world whose location can be estimated by a sensor—such as wifi access points or radio beacons. Raw-data approaches make no assumption that landmarks can be identified, and instead model directly as a function of the location.

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