Ever wonder how a robot navigates an unfamiliar environment, like a dark forest, without getting lost? In this video, we break down Simultaneous Localization and Mapping (SLAM), one of the most fundamental capabilities required for true robotic autonomy.

We explore the classic "chicken and egg" problem of robotics: how a robot needs a map to know its location, but needs to know its location to build that map.

In this video, we cover:

• The 5 Core Components of SLAM: From sensor data processing and odometry to mapping, data association, and the magic of loop closure (which corrects accumulated drift to keep the map consistent).
• Classical vs. Modern SLAM: We trace the evolution of SLAM from early probabilistic filters l ...