News: Updated Sept. 17. Click here to see the requirements of your linux laptop to run our software.
News: Click here to see the location of the tutorial.
News: We will distribute our software on CDROM and USB stick to you.
Robotic mapping addresses the problem of acquiring
spatial models of physical environments through mobile robots. Maps
are commonly used for robot navigation (e.g., localization) and in a
wide variety application areas. The robotic mapping problem is
commonly referred to as SLAM (simultaneous localization and mapping)
or CML (concurrent mapping and localization).
If the robot poses were known, the local sensor
inputs of the robot, i.e., local maps, could be registered into a
common coordinate system to create a map. Unfortunately, any mobile
robot's self localization suffers from imprecision. Therefore, the
structure of the local elements, e.g., of single scans, needs to be
used to create a precise global map. On the other hand, it is
relatively easy to localize a robot if the environment is know, i.e.,
a map is provided.
The nature of SLAM is the one of a chicken-egg problem, namely the
coordinates of the elements of the map depend on the robots postion,
and the robots position is estimates with the usage of the map, you
This tutorial provides a state of the art
introduction to the SLAM problem. The tutorial
targets on the users side, i.e., people who's research field is
robotics, but not SLAM. Since robotic maps are needed in so many
applications, our goal is to enable non-SLAM people to use SLAM
softwares and to gain hands-on experiences on the problems occurring,
when trying to solve SLAM.
The tutorial consists of presentations, software demonstrations and
hands-on experience on our software!
To participate at the full-day tutorial please register for the ECMR
Things to bring along
Please bring our own Linux-Laptop (!!!) to be able to run our software!
Note Windows won't be supported in this tutorial. The following packages should be installed in order to run our software:
Programm and Material
In this first part of the tutorial, you will learn how to use particle
filter applications for mobile robot navigation. These tools include
the Carnegie Mellon Robot Navigation Toolkit (CARMEN),
and a Rao-Blackwellized particle filter for learning accurate
grid maps GMapping.
After providing the general concepts of particle filters (see also
introduction to particle filters). We will explain the usage on
how to use the mapper system for creating maps based on real robot
data. These maps will then be used for navigating a robot using
This second part of the tutorial focuses on how to view different
variants of SLAM as a least square problem represented by a graph. This
view is very generic being applicable to 2D and 3D SLAM based on
laserscan-matching or (visual) feature extraction. You will first learn,
how to define your SLAM problem as a so called information matrix and
how the sparsity pattern (which entries are 0) of that matrix encodes
its the structure as a graph. Then you will learn, how this structure
can be used to tremendously speed up computation. Finally, in the
practical lab session, you will use the treemap algorithm provided as an
open-source library to efficiently solve an extremely large SLAM problem
with this approach.
In this third part of the tutorial you will get hands-on experience to
3D scan matching and the performance issues that come along with
matching large point clouds. In addition, you will learn about 6D
SLAM, i.e., global consistent 3D scan matching using the six degrees
of freedom in the robot pose.
Given 3D scans of the environment, for instance aquired with a tilting
or rotating laser range finder, the 6D SLAM software merges the
recorded scans from its local into a common coordinate system. A
variant of the widely used Iterative Closest Points Algorithm (ICP) is
used for that purpose. Several algorithmic stategies for speeding up
the computational requirements can be explored. Based on the matched
scans a graph containing the relations is built and a global
relaxation scheme creates a overall consistent map. As result the
software presents precise 3D maps of the environment.
Download: Slides Software and Data
Giorgio Grisetti is working as a
Post-doc at the research lab for Autonomous Intelligent Systems headed
by Wolfram Burgard at the University of Freiburg. His research
interests lie in the areas of, SLAM, mobile robot localization, and
probabilistic state estimation. He was a PhD student at University of
Rome "La Sapienza" in the Intelligent Systems Lab. His advisor was
Daniele Nardi and he received his PhD degree in April 2006. His PhD
thesis focused on SLAM using Rao-Blackwellized particle filters. In
2001, he received his M.Sc. degree in computer engineering, at the
University of Rome.
Together with Dr. Cyrill Stachniss he developed the open source "GMapping"
package for 2D mapping.
Cyrill Stachniss is currently working as a Postdoc in the Lab for
Autonomous Intelligent Systems at the University of Freiburg. Up to
November 2006, he was a senior researcher at the Swiss Federal
Institute of Technology (ETH) in Zurich. From 2002 to April 2006, he
joined Wolfram Burgards lab in Freiburg. In April 2006 he received his
PhD at the Department of Computer Science at the University of
Freiburg. The title of the thesis is "Exploration and Mapping with
Mobile Robots" . Currently, he is working on exploration techniques
for mobile robots in combination with SLAM. He is furthermore
interested in computer controlled cars and scene analysis.
Udo Frese is a senior research at the German Center
for Artificial Intelligence (DFKI) in Bremen, He is also member of the
Sonderforschungsbereich SFB/TR 8 Spatial Cognition located at
University of Bremen and Freiburg. Past affiliations include the
German Aerospace Center in Oberpfaffenhofen. He received his PhD
degree in May 2004 at University of Erlangen-Nürnberg with his
thesis titled "An O(log n) Algorithm for Simultaneous Localization and
Mapping (SLAM) of Mobile Robots in Indoor Environments". Currently he
is working in visual SLAM and in real-time computer vision for sport
Andreas Nüchter is a research associate
at University of Osnabrück. Past affiliations were with the
Fraunhofer Institute for Autonomous Intelligent Systems (AIS, Sankt
Augustin) and University of Bonn, from which he received the diploma
degree in computer science from University of Bonn in 2002 and was
awarded with the best paper award by the German society of informatics
(GI) for his thesis. He holds a doctorate degree (Dr. rer. nat) from
University of Bonn. His research interests include reliable robot
control, 3D environment mapping, 3D vision, and laser scanning
technologies. He is a member of the GI and the IEEE.
Andreas has devoloped fast 3D scan matching algorithms, enabling
robots to map their environment in 3D using 6 degrees of freedom for
pose estimates. He showed the capabilities of his robotic SLAM
approach at RoboCup Rescue competitions, ELROB and several other
The SLAM Tutorial will take place on Tuesday, the 18th of
September. Each of the three parts will contain a lecture of about one
hour, followed by software demonstrations and programming sessions.
|10:00 - 12:00
by Giorgio Grisetti and Cyrill Stachniss,
University of Freiburg, Germany
|13:00 - 15:30
by Udo Frese,
University of Bremen, Germany
|16:00 - 18:00
by Andreas Nüchter
University of Osnabrück, Germany
The tutorial will be held in
Building 101 (same as the conference)
Directions can be found on the conference website.
room 016 / 018
Further Information about SLAM
Please refer to Sebastian state of
the art review, Udo's homepage
or the openslam.org page to obtain
further information about SLAM.
University of Osnabrück
Institute of Computer Science