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Introduction to Mobile Robotics - SS 2016

Introduction to Mobile Robotics (engl.) - Autonomous Mobile Systems

This course will introduce basic concepts and techniques used within the field of mobile robotics. We analyze the fundamental challenges for autonomous intelligent systems and present the state of the art solutions. Among other topics, we will discuss:
  • Kinematics
  • Sensors
  • Vehicle localization
  • Map building
  • SLAM
  • Path planning
  • Exploration of unknown terrain


Lectures

Lecture Dates Topic Slides Recordings
00 20-04-2016 Introduction PDF MP4
01 22-04-2016
27-04-2016
Linear Algebra PDF MP4
MP4
02 27-04-2016 Robot Control Paradigms PDF MP4
03 27-04-2016
29-04-2016
Wheeled Locomotion PDF MP4
MP4
04 29-04-2015 Proximity Sensors PDF MP4
05 04-05-2016
04-05-2016
06-05-2016
11-05-2016
Probabilistic Robotics PDF MP4
MP4
MP4
MP4
06 11-05-2016
13-05-2016
25-05-2016
Probabilistic Motion Models PDF MP4
MP4
MP4
07 25-05-2016
27-05-2016
Probabilistic Sensor Models PDF MP4
MP4
08 01-06-2016 Bayes Filter - Discrete Filters PDF MP4
09 01-06-2016
03-06-2016
Bayes Filter - Particle Filter and MCL PDF MP4
MP4
10 08-06-2016 Bayes Filter - Kalman Filter PDF MP4
11 08-06-2015 Bayes Filter - Extended Kalman Filter PDF MP4
12 10-06-2016
15-06-2016
Grid Maps and Mapping With Known Poses PDF MP4
MP4
13 15-06-2016 SLAM - Simultaneous Localization and Mapping PDF MP4
14 17-06-2016 SLAM - Landmark-based FastSLAM PDF MP4
15 22-06-2016
22-06-2016
SLAM - Grid-based FastSLAM PDF MP4
MP4
16 24-06-2016 SLAM - Graph-based SLAM PDF MP4
17 29-06-2016 Techniques for 3D Mapping PDF MP4
MP4
18 29-06-2016 Iterative Closest Point Algorithm PDF MP4
19 01-07-2016
06-07-2016
08-07-2016
13-07-2016
Path and Motion Planning PDF MP4
MP4
MP4
MP4
20 15-07-2016 Multi-Robot Exploration PDF MP4
21 20-07-2016 Information Driven Exploration PDF MP4
22 22-07-2016 Summary PDF MP4

Exercises

Solving the exercise sheets is recommended but not mandatory to be admitted to the final exam. There are no bonus points.

Exercise sheets will be published on Fridays and will be discussed in class one week later. We strongly encourage you to solve the exercise sheets beforehand to benefit from the discussions in class.

Decide if you prefer to use Octave or Python and join the corresponding exercise group:
Octave: Group 1 - Room: SR 00-010/014 - Organized by: Ayush Dewan, Marina Kollmitz
Python: Group 2 - Room: HS 00 026 µ - Organized by: Tayyab Naseer, Tim Caselitz

Join this Google group forum for discussing questions on lectures and exercises or send a mail to mobilerobotics@informatik.uni-freiburg.de for an appointment with one of the teaching assistants.

Sheet Due date Topic Octave Python Download
01 22-04-2016 Setup Octave / Python PDF PDF
02 29-04-2016 Linear Algebra PDF PDF laserscan.dat
03 06-05-2016 Locomotion, Differential Drive PDF PDF
04 13-05-2016 Bayes Rule PDF PDF
05 27-05-2016 Sampling, Motion Models PDF PDF
06 03-06-2016 Sensor Models PDF PDF
07 10-06-2016 Discrete Filter, Particle Filter PDF PDF pf_framework_octave.tar.gz
pf_framework_python.tar.gz
08 17-06-2016 Extended Kalman Filter PDF PDF ekf_framework_octave.tar.gz
ekf_framework_python.tar.gz
09 24-06-2016 Mapping with Known Poses PDF PDF
10 01-07-2016 Simultaneous Localization and Mapping PDF PDF
11 08-07-2016 FastSLAM PDF PDF fastslam_framework_octave.tar.gz
fastslam_framework_python.tar.gz
12 15-07-2016 Iterative Closest Point Algorithm PDF PDF icp_framework_octave.tar.gz
icp_framework_python.tar.gz
13 22-07-2016 Path Planning PDF PDF planning_framework_octave.tar.gz
planning_framework_python.tar.gz


Additional Material

  • Notes on one dimensional Gaussians (PDF)
  • Notes on multi dimensional Gaussians (PDF)
  • Octave cheat sheet (PDF)
  • Python cheat sheet (PDF)
  • Matrix cookbook (PDF)
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