Robot Mapping

The Course

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, mapping and localization. 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, and data association. 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.

I have tried to acknowledge people from whom is used slides, image, or video material. In case I have missed something, please let me know. Feel free to use and change the slides. The intention behind sharing my material with other lecturers to reduce the workload when preparing lectures. If you adapt this course material, please make sure you keep the acknowledgements to other people’s work. If you use the slides, I appreciate an acknowledgement and – to satisfy my own curiosity – please send me a short email notice.

 

Slides

PDF files and Powerpoint (pptx) slides are available for download.

 

Video Recordings

Video Recordings are available through YouTube: 2015/16 Playlist

 

igg
Institute of Geodesy
and Geoinformation
lwf
Faculty of Agriculture
ubn
University of Bonn