The problem of learning maps is an important problem in mobile robotics

Models of the environment are needed for a series of applications such as transportation, cleaning, rescue, and various other service robotic tasks.

Learning maps requires solutions to two tasks, namely basic mapping and localization.

Basic mapping is the problem of integrating the information gathered with the robot's sensors into a given representation. It can intuitively 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?'' These

two tasks cannot be solved independently of each other. Solving both problems jointly is often referred to as the simultaneous localization and mapping (SLAM) problem

There are several variants of the SLAM problem including passive and active approaches, topological and metric SLAM, feature-based vs. volumetric approaches, and may others

The lecture will cover different topics and techniques in the context of environment modeling with mobile robots.
 We will cover techniques such as SLAM with the family of Kalman filters, information filters, particle filters.
 We will furthermore investigate graph-based approaches, least-squares error minimization, techniques for place recognition and appearance-based mapping, data association as well as information-driven approaches for observation processing.
The exercises and homework assignments will also cover practical hands-on experience with mapping techniques, as basic implementations will be part of the homework assignments