ELEC-E8111 - Autonomous mobile robots L, 21.02.2017-29.03.2017

Materials

• Materials
  

  Materials

  • Lecture 1: Feb 21, 2017 Folder

    Course introduction and arrangements

    Introduction to mobile robotics and subsystems

    
    

  • Lecture 2: Feb 22, 2017 Folder

    Introduction to design cases and lab work arrangements

    Introduction to ROS

  • Lecture 3, Feb 28, 2017 Folder

    Locomotion, Kinematics, and Low level
    Motion Control
    

  • ROS lab guide File

    Here is the tutorial guide you will use for the lab involving ROS and the Pioneer robot. You do not need to print it, rather it is recommended that you download it on the computer in the lab, since copy-paste will be useful.
    

  • Lecture 4: March 1, 2017 Folder

    Localization, mapping and SLAM = Simultaneous Localization and Mapping, idea

    - Main material as subset of ETH slides

    - Kalman-filter based traditional vehicle localization in the tractor-trailer -case.

    
    

  • Lecture 5: March 7, 2017 Folder

    Sensing and perception, overview.
    

  • GUIDELINES, RULES AND FORMATS FOR THE PIONEER-ROBOT PROJECT REPORT AND ROBOT DESIGN TASK Folder

    The first file contains the formats of the report for the Pioneer-robot project, i.e. testing algorithms under ROS on Pioneer Robot platform.

    The second file contains the rules, guidelines and formats of the Robot design task: Design of a case robot system. The cases are:
    

    1.Indoor: ball fetching/collecting service robot for tennis courts
    2.Indoor: borderline painting service robot for exhibition halls
    3.Outdoor: Autonomous bus for fixed bus routes in cities

    4.Outfoor: Autonomous drone for mapping spatial occurrence of special weeds in crop farming (for instance common wild oath)
    

  • Lecture 6, March 8, 2017 Folder

    Extended Kalman Filter SLAM (EKF SLAM)

    A  video  haehnel-RawOdometry-anim.avi demonstrates mapping with raw odometry localization  - Just mapping, not SLAM. I odometry errors accumulate quickly too large.

    A video haehnel-ScanMatching-anim.avi demonstrates  first real SLAM, localization on the basis of laser scan matching, only one estimate for pose, Mapping works

    file slam.pdf, EKF SLAM video can be run  in the slam 2017.ppt -slide set
    

    Particle filter and  Fast SLAM

    Particle filters are introduced in particle-filters 2017.pdf and .ppt.

    Fast SLAM: You can run videos in the fast SLAM 2017.ppt -slide set. pdf available, too.
    

    Some Fast SLAM videos added as separate files.

    bruceton-one-loop.avi ; fastslam-dmb-fastslam.avi ; haehnel-ScanMatchingFastSLAM-Seattle-anim-2.avi
    

    
    
  • Lecture 7, March 15 Folder

    Graph SLAM
    

    Good introduction about GraphSLAM can be found in Giorgio Grisetti, Rainer Kummerle, Cyrill Stachniss & Wolfram Burgard , A Tutorial on Graph-Based SLAM, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany. Graph SLAM is quite complicate, just try to understand the main ideas.
    

    http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf

    As additional reading material, general tutorial papers about SLAM can be found in
    

    Durrant-Whyte, H., & Bailey, T. (2006). Simultaneous localization and mapping: part I. Robotics & Automation Magazine, IEEE, 13(2), 99-110.
    Bailey, T., & Durrant-Whyte, H. (2006). Simultaneous localization and mapping (SLAM): Part II. IEEE Robotics & Automation Magazine, 13(3), 108-117.
    
    

    
    

  • Lecture 8, March 16, 2017 Folder

    Visual localization and motion estimation

    
    

  • Lecture 9: March 21, 2017 Folder

    Inertial navigation systems

    Material will be edited, not final.
    

    
    

  • Lecture 10, March 22, 2017 Folder

    Mathematics of Pose Estimation (Temporary material, will be shortened))
    

    Satellite Navigation Systems
    

  • Lecture 11, March 28, 2017 Folder

    Path and Motion  Planning, Indoor.

    Path and Motion Planning, Outdoor.
    

  • Lecture 12, March 29, 2017 Folder

    Path and Motion Planning Continues

    Example of Nonlinear Model Predictive Control ( NMPC) applied in Motion control of Tractor and Implement.