01. Introduction to SLAM

Simultaneous Localization and Mapping

In the previous lessons, you learned all about localization methods that aim to locate a robot or car in an environment given sensor readings and motion data, and we would start out knowing close to nothing about the surrounding environment. In practice, in addition to localization, we also want to build up a model of the robot's environment so that we have an idea of objects, and landmarks that surround it and so that we can use this map data to ensure that we are on the right path as the robot moves through the world!

In this lesson, you'll learn how to implement SLAM (Simultaneous Localization and Mapping) for a 2 dimensional world! You’ll combine what you know about robot sensor measurements and movement to create locate a robot and create a map of an environment from only sensor and motion data gathered by a robot, over time. SLAM gives you a way to track the location of a robot in the world in real-time and identify the locations of landmarks such as buildings, trees, rocks, and other world features.

This is an active area of research in the fields of robotics and autonomous systems, so let's get started!