Introduction to Mobile Robotics - SS 2008
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
- Lecturers: Prof. Dr. Wolfram Burgard, Dr. Cyrill Stachniss, Dr. Giorgio Grisetti, Dr. Maren Bennewitz
- Co-organizers: Barbara Frank, Dominik Joho, Hauke Strasdat
- Tuesday 11-13, Friday 11-12, Room: SR 01-009, Building 101
- Classes / exercises: Friday 12-13, Room: SR 01-009, Building 101
- Question Time: Wednesday, 8. 10., 11-12, Room: SR 01-016, Building 101. You will have the chance to discuss open questions regarding this lecture and the exam.
Exercises
Attention: Rules for earning bonus points have changed! See updated sheet 1.Note: there is a FAQ (frequently asked questions) for the exercises/lab assignments.
- Exercise sheet 1 – Drives, Odometry, Bayes (ZIP, PDF) updated 30. April
- Exercise sheet 2 – Bayes Filter, Motion Models (PDF)
- Exercise sheet 3 – Data Explanation, Sensor Models, Discrete Filter (ZIP, PDF)
- Exercise sheet 4 – Particle Filter (ZIP, PDF)
- Exercise sheet 5 – Kalman Filter (PDF)
- Exercise sheet 6 – Grid Mapping, Extended Kalman Filter (ZIP, PDF)
- Exercise sheet 7 – Grid Mapping, SLAM (ZIP, PDF)
- Exercise sheet 8 – EKF Localization, Bearing-Only SLAM (PDF)
- Exercise sheet 9 – Path Planning, ICP (PDF)
- Exercise sheet 10 – Exploration (ZIP, PDF)
- Exercise sheet 11 – Clustering, Gaussian Process Regression (ZIP, PDF) updated 16. July
Slides (Update)
- Introduction PDF
- Paradigms PDF
- Locomotion PDF
- Sensors PDF
- Probabilities and Bayes PDF
- Probabilistic Motion Models PDF
- Probabilistic Sensor Models PDF
- Bayes Filter - Discrete Filters PDF
- Bayes Filter - Particle Filter and Monte Carlo Localization PDF
- Bayes Filter - Kalman Filter PDF
- Mapping with Known Poses PDF
- SLAM: Simultaneous Localization and Mapping PDF
- SLAM - Landmark-based FastSLAM PDF
- SLAM & Grid-based FastSLAM PDF
- Path Planning and Collision Avoidance PDF
- Iterative Closest Point Algorithm PDF
- Place and People Recognition with Mobile Robots PDF
- Mapping with Elevation Maps PDF
- Multi-Robot Exploration PDF
- Improved Multi-Robot Exploration PDF
- Information Gain-Based Exploration PDF
- Gaussian Processes PDF
- Clustering PDF
- Summary PDF
Recordings
- Wheeled Locomotion (25.04.08)
- Proximity Sensors (25.04.08)
- Probabilistic Robotics (1) (29.04.08)
- Probabilistic Robotics (2) (02.05.08)
- Probabilistic Robotics (3) (06.05.08)
- Probabilistic Motion Models (1) (06.05.08)
- Probabilistic Motion Models (2) (09.05.08)
- Probabilistic Sensor Models (1) (09.05.08)
- Probabilistic Sensor Models (2) (20.05.08)
- Discrete Filters (23.05.08)
- Bayes Filter - Particle Filter and Monte Carlo Localization (27.05.08)
- Bayes Filter - Kalman Filter (1) (30.05.08)
- Bayes Filter - Kalman Filter (2) (03.06.08)
- Mapping with Known Poses (1) (03.06.08)
- Mapping with Known Poses (2) (06.06.08)
- Mapping with Known Poses (3) (10.06.08)
- SLAM: Simultaneous Localization and Mapping (10.06.08)
- SLAM - Landmark-based FastSLAM (13.06.08)
- SLAM & Grid-based FastSLAM (17.06.08)
- Path Planning and Collision Avoidance (20.06.08)
- Iterative Closest Point Algorithm (24.06.08)
- Place and People Recognition with Mobile Robots (27.06.08)
- Mapping with Elevation Maps (01.07.08)
- Multi-Robot Exploration (1) (01.07.08)
- Multi-Robot Exploration (2) (08.07.08)
- Improved Multi-Robot Exploration (08.07.08)
- Information Gain-based Exploration (1) (08.07.08)
- Information Gain-based Exploration (2) (11.07.08)
- Gaussian Processes (15.07.08)
- Clustering (18.07.08)
- Summary (1) (22.07.08)
- Summary (2) (22.07.08)