Graduate Courses – 网络机器人与系统实验室

移动机器人导航理论/Navigation of Autonomous Robots

Course Outline（2020-Now）：
- 1. Introduction to Probabilistic Robotics
- 2. Bayes Filters and Kalman Filter (EKF, IKF, UKF)
- 3. Kalman Filter-based Localization
- 4. Discrete Filters: Histogram Filter，Particle Filter
- 5. Partilce Filter Application：Monte Carlo Localization
- 6. SLAM Introduction and Rigid Body Motion
- 7. EKF-based Simultaneous Localization and Mapping
- 8. Lie Group & Lie Algebra
- 9. Nonlinear Optimization
- 10. Visual Odometry: Direct & Feature-based
- 11. Visual Odometry: Front-end & Back-end
- 12. Visual-inertial Odometry: IMU preintegration
- 13. BoW: Visual Loop Detection
- 14. LiDAR-based SLAM: LOAM-LegoLOAM-LIO-SAM-LVI-SAM
- 15. Motion Planning
- 16. Field Potential and Optimization-based Path Planning & Final Project
Textbook:
- Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard, Dieter Fox；
- State Estimation for Robotics

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