Simultaneous localization and mapping (SLAM)

From Computer Science Wiki

Simultaneous Localization and Mapping, or SLAM, is a computational problem in the field of robotics. As the name implies, it's about doing two things at the same time:

  1. Localization: Determining where a robot is located in an environment.
  2. Mapping: Building a map of that environment.

What makes SLAM challenging is that it's a chicken-and-egg problem: to know where you are (localization), you need a map, but to create a map, you need to know where you are. SLAM is the process of solving these two problems simultaneously.

Here's a simplified explanation of how SLAM works:

  1. The robot starts in an unknown location in an unknown environment. It uses its sensors (like cameras, LIDAR, or wheel encoders) to gather data about its surroundings and its own motion.
  2. Based on this data, the robot makes an initial guess about its location and about the structure of the environment.
  3. As the robot moves and gathers more data, it updates its guesses. For example, if it sees a wall in front of it and then moves to the right, it updates its guess about its own location (it thinks it's moved to the right) and its map (it adds a wall to the map).
  4. Over time, the robot's guesses become more and more accurate, especially as it revisits the same locations (a process called loop closure), until it has a good estimate of both its own location and the structure of the environment.

SLAM is a critical capability for many autonomous robots, especially those that operate in unknown environments, like rescue robots. With SLAM, a rescue robot could navigate through a collapsed building, creating a map of the building as it goes, and use this map to locate victims, identify safe paths, and return to base.