SLAM a short for Simultaneous Localization and Mapping. This method is ideal for autonomous vehicles that allow you to develop a map and then localize your car on it simultaneously. The algorithms enable the vehicle so that it can map out environments that are unknown to it. Apart from this, engineers make use of the map information in order to perform different tasks like obstacle avoidance and path planning. Read on to find out more.
Why SLAM Matters
For many years, SLAM has been used to perform technical research. However, since computer processing speed has exponentially increased and low-cost sensors have been made available, SLAM is used for a number of practical applications in different fields.
An example of a SLAM is a robot vacuum. In the absence of SLAM, the robot vacuum will move around randomly. As a result, it won’t be able to clean the entire room. Apart from this, this approach may consume a lot more power and the battery will run out much faster.
On the other hand, SLAM-based robots can enable the vacuum to perform better. Actually, this technology uses technical information, such as the number of revolutions that come from the imaging sensors and cameras. This is known as localization and prevents the machine from going over the same place twice.
SLAM is quite useful in other areas of application like parking a car and navigating mobile robots, just to name a few.
How SLAM Works
Generally, two types of components are used for this lamb Technology. The first type is known as sensor signal processing which includes different types of processing. This type of processing depends upon the sensors employed. This technology involves pose graph optimization, which includes back-end processing.
Visual SLAM is also known as vSLAM. It makes use of images from image sensors and cameras. It implies simple cameras, such as spherical cameras, fisheye cameras and wide-angle cameras, just to name a few.
The great thing about visual SLAM is that it can be implemented without spending a lot of money. Besides, since cameras offer a lot of information, you can use them to detect landmarks. It is possible to combine landmark detection with graph-based optimization.
Monocular SLAM refers to a system that uses only one camera. Therefore, it is difficult to define depth, which can be solved through the detection of AR markers and checkerboards.
Visual SLAM algorithms can fall into two categories: sparse methods and dense methods. The first one makes use of algorithms like ORB-SLAM and PTAM. The later uses the image brightness and other algorithms like SVO, DSO, LSD-SLAM, and DTAM.