SLAM is the abbreviation for Simultaneous Localization And Mapping.
Mapping is the problem of integrating the information gathered with the robot's sensors into a given representation. It can be described by the question ``What does the world look like?'' Central aspects in mapping are the representation of the environment and the interpretation of sensor data. In contrast to this, localization is the problem of estimating the pose of the robot relative to a map. In other words, the robot has to answer the question, ``Where am I?'' Typically, one distinguishes between pose tracking, where the initial pose of the vehicle is known, and global localization, in which no a priori knowledge about the starting position is given.
Simultaneous localization and mapping (SLAM) is therefore defined as the problem of building a map while at the same time localizing the robot within that map. In practice, these two problems cannot be solved independently of each other. Before a robot can answer the question of what the environment looks like given a set of observations, it needs to know from which locations these observations have been made. At the same time, it is hard to estimate the current position of a vehicle without a map. Therefore, SLAM is often referred to as a chicken and egg problem: A good map is needed for localization while an accurate pose estimate is needed to build a map.