• Course Outline(2018-2019):
    • 1. Introduction to Mobile Robots
    • 2. Introduction to Probabilistic Robotics
    • 3. Bayes Filter Implementations Kalman Filters
    • 4. Kalman Filter Implementations Localization
    • 5. Bayes Filter Implementations: Discrete Filters: Histogram Filter,Particle Filter
    • 6. Partilce Filter Application:Monte Carlo Localization
    • 7. Simultaneous Localization and Mapping
    • 8. Rigid Body Motion
    • 9. Lie Group & Lie Algebra
    • 10. Camera Model & Nonlinear Optimization
    • 11. Visual Odometry: Direct & Feature-based
    • 12. Front-end & Back-end
    • 13. Loop Detection
    • 14. Mapping & SLAM future
    • 15. Motion Planning & Final Project
  • Textbook:
    • Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard, Dieter Fox;
    • State Estimation for Robotics
  • Navigation of Mobile Robots. (2011-2017)
    • Contents: Introduction of different kinds of mobile robots, Parameter/Non Parameter based Bayes Filters, Monte Carlo Localization, EKF based Localization, EKF-SLAM, FastSLAM, RGBD-SLAM, and Sample-based Motion Planning

计算机视觉/Computer Vision

  • Course time (2009-2010)
  • Textbook:    Digital Image Processing, Kenneth R. Castleman.
  • Computer Vision: a modern approach,David A. Forsyth, & Jean Ponce.