README.md

Autonomous Mobile Robots

Course Structure 👾

• 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