SLAM in a nutshell

SLAM is a "Hard" problem?!

SLAM stands for Simultaneous Localization and Mapping. It’s how robots explore unknown spaces; building a map while figuring out where they are inside it.

Using sensors like LiDAR or cameras, they piece together the world step by step, with no GPS or prior knowledge needed.

The tricky part? It’s a chicken-or-egg problem. To localize, the robot needs a map. But to build a map, it needs to know where it is. SLAM has to solve both at once, in real time, with noisy data and a constantly changing world. That’s what makes it one of the toughest challenges in robotics.

Let's take a look at some examples (maps built by 3D-LiDAR (right image) and RGBD camera (left images)):

If you want to become a true expert in SLAM, do not miss my SLAM package (from my SLAM workshop):

Go to the page

As an example, in the next section you can find two of the main topics of this workshop

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SLAM Paradigms

Kalman Filter Family
Particle Filter Family
Graph Optimization Family

Localization + Mapping

Localization
Mapping
SLAM (Simultaneous Localization & Mapping)

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