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Introduction to Mobile Robotics - SS 2014

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:
  • Sensors,
  • Kinematics,
  • Path planning,
  • Vehicle localization,
  • Map building,
  • SLAM,
  • Exploration of unknown terrain



Exercises

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

The exercises should be solved in groups of two students. Submit a single tar/zip file with all codes, scripts and a single pdf with all answers (programs and figures).

In general, assignments will be published on Friday and have to be submitted the following Thursday before midnight. Submit programming exercises via email to mobilerobotics@informatik.uni-freiburg.de

Join this forum for discussing questions on exercises and lectures
https://groups.google.com/forum/#!forum/ais_introtorobotics14

  • Exercise sheet 1 – Setup (Octave tutorial) (PDF, Cheat sheet)
  • Exercise sheet 2 – Linear Algebra, Locomotion, and Sensing (PDF, laserscan.dat)
  • Exercise sheet 3 – Locomotion and Bayes Rule (PDF)
  • Exercise sheet 4 – Sampling, Motion Models (PDF)
  • Exercise sheet 5 – Sensor Models (PDF)
  • Exercise sheet 6 – Mapping With Known Poses (PDF)
  • Exercise sheet 7 – Extended Kalman Filter (PDF, EKF Framework)
  • Exercise sheet 8 – Velocity Motion Model, Discrete Filter (PDF)
  • Exercise sheet 9 – Particle Filter (PDF, PF Framework)
  • Exercise sheet 10 – SLAM (PDF)
  • Exercise sheet 11 – FastSLAM (PDF, FastSLAM Framework)
  • Exercise sheet 12 – ICP and Recapitulation (PDF, ICP Framework)



Slides

  • Linear Algebra (PDF)
  • Robot Control Paradigms (PDF)
  • Wheeled Locomotion (PDF)
  • Proximity Sensors (PDF)
  • Probabilistic Robotics (PDF)
  • Motion Models (PDF)
  • Probabilistic Sensor Models (PDF)
  • Mapping With Known Poses (PDF)
  • Kalman Filter (PDF)
  • Extended Kalman Filter (PDF)
  • Discrete Filters (PDF)
  • Particle Filter and Monte Carlo Localization (PDF)
  • SLAM: Simultaneous Localization and Mapping (PDF)
  • SLAM: Landmark-based FastSLAM (PDF)
  • SLAM: Grid-based FastSLAM (PDF)
  • Techniques for 3D Mapping (PDF)
  • Iterative Closest Points Algorithm (PDF)
  • Path Planning and Collision Avoidance (PDF)
  • Information Driven Exploration (PDF)
  • Summary (PDF)



Recordings

  • 02.05.2014 – Linear Algebra (MP4)
  • 07.05.2014 – Robot Control Paradigms (MP4)
  • 07.05.2014 – Wheeled Locomotion (MP4)
  • 09.05.2014 – Wheeled Locomotion (MP4)
  • 09.05.2014 – Proximity Sensors (MP4)
  • 14.05.2014 – Probabilistic Robotics (MP4)
  • 14.05.2014 – Probabilistic Robotics (MP4)
  • 16.05.2014 – Probabilistic Robotics (MP4)
  • 21.05.2014 – Probabilistic Robotics, Probabilistic Motion Models (MP4)
  • 23.05.2014 – Probabilistic Motion Models (MP4)
  • 28.05.2014 – Probabilistic Motion Models, Probalistic Sensor Models (MP4)
  • 30.05.2014 – Probabilistic Sensor Models (MP4)
  • 04.06.2014 – Mapping With Known Poses (MP4)
  • 18.06.2014 – Kalman Filter (MP4)
  • 20.06.2014 – Extended Kalman Filter (MP4)
  • 25.06.2014 – Extended Kalman Filter, Discrete Filters (MP4)
  • 27.06.2014 – Discrete Filters (MP4)
  • 02.07.2014 – Particle Filter and Monte Carlo Localization (MP4)
  • 04.07.2014 – Particle Filter and Monte Carlo Localization (MP4)
  • 09.07.2014 – SLAM: Simultaneous Localization and Mapping (MP4)
  • 11.07.2014 – SLAM: Landmark-based FastSLAM (MP4)
  • 16.07.2014 – SLAM: Landmark-based FastSLAM (Part 2) and Grid-Based FastSLAM (MP4)
  • 18.07.2014 – SLAM: Grid-Based FastSLAM (Part 2) (MP4)
  • 23.07.2014 – Techniques for 3D Mapping (MP4)
  • 23.07.2014 – Iterative Closest Point Algorithm (MP4)
  • 25.07.2014 – Path Planning and Collision Avoidance (MP4)
  • 30.07.2014 – Path Planning and Collision Avoidance (MP4)
  • 30.07.2014 – Information Driven Exploration (MP4)
  • 01.08.2014 – Summary (MP4)



Additional Material

Benutzerspezifische Werkzeuge