Section 0 👽

• Introduction 📚

Section 1 (Control) 👽

• Motion Control 📚
  • Kinematics of wheeled mobile robots: internal, external, direct, and inverse
  • Differential drive kinematics
  • Bicycle drive kinematics
  • Rear-wheel bicycle drive kinematics
  • Car(Ackermann) drive kinematics
  • Wheel kinematics constraints: rolling contact and lateral slippage
  • Wheeled Mobile System Control: pose and orientation
    • Control to reference pose
    • Control to reference pose via an intermediate point
    • Control to reference pose via an intermediate direction
    • Control by a straight line and a circular arc
    • Reference path control
  • Lateral control (Geometric controls)
    • The pure pursuit (or pure tracking controller)
    • Stanley controller
• Dubins path planning 📚

Section 2 (Estimation) 👽

• Bayesian Filter 📚

  • Basic of Probability
  • Probabilistic Generative Laws
  • Estimation from Measurements
  • Estimation from Measurements and Controls

• Kalman filter 📚

  • Gaussian Distribution
  • One Dimensional Kalman Filter
  • Multivariate Density Function
  • Marginal Density Function
  • Multivariate Normal Function
  • Two Dimensional Gaussian
  • Multiple Random Variable
  • Multidimensional Kalman Filter
  • Sensor Fusion
  • Linearization, Taylor Series Expansion, Linear Systems
  • Extended Kalman Filter (EKF)
  • Comparison between KF and EKF

• Particle Filter 📚

  • A Taxonomy of Particle Filter
  • Bayesian Filter
  • Monte Carlo Integration (MCI)
  • Particle Filter
  • Importance Sampling
  • Particle Filter Algorithm

• Robot localization 📚

  • A Taxonomy of Localization Problems
  • Markov localization
  • Environment Sensing
  • Motion in the Environment
  • Localization in the Environment
  • EKF localization with known correspondence
  • Particle filter localization with known correspondence

• Robot mapping 📚

  • Ray casting and ray tracing
  • Ray-casting algorithm
  • Winding number algorithm
  • TODO (more to come)

• Robot simultaneous localization and mapping (SLAM) 📚

  • Introduction
  • TODO (more to come)

Section 3 (Perception) 👽

• Line Extraction Techniques 📚
  • Hough Transformation
  • Split-and-Merge Algorithm
  • Line Regression Algorithm/li>
• Similarity Measurements 📚
  • Edge Detection (based on derivative and gradient)
  • Corner Detection
  • The Laplace Operator
  • Laplacian of Gaussian (LoG)
  • Difference of Gaussian (DoG)
  • Gaussian and Laplacian Pyramids
  • Scale Invariant Feature Transform (SIFT)
  • Scale-space Extrema Detection
  • Keypoint Localization
  • Orientation Assignment
  • Keypoint Descriptor

• Monocular Vision 📚

  • Pinhole Camera Model
  • Image Plane, Camera Plane, Projection Matrix
  • Projective transformation
  • Finding Projection Matrix using Direct Linear Transform (DLT)
  • Camera Calibration

• Stereo Vision 📚

  • Simple Stereo, General Stereo
  • Some homogeneous properties
  • Epipolar Geometry
  • Essential matrix, Fundamental matrix
  • Camera Calibration
• Depth Estimation

References [:books:]

• Robert Grover Brown, Patrick YC Hwang, et al. Introduction to random signals and applied Kalman filtering, volume 3. Wiley New York, 1992.
• Gregor Klancar, Andrej Zdesar, Saso Blazic, and Igor Skrjanc. Wheeled mobile robotics: from fundamentals towards autonomous systems. Butterworth-Heinemann, 2017.
• Roland Siegwart, Illah Reza Nourbakhsh, and Davide Scaramuzza. Introduction to autonomous mobile robots. MIT press, 2011.
• Sebastian Thrun. Probabilistic robotics. Communications of the ACM, 45(3):52–57, 2002.
• https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python