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Algorithmic and Computational Foundations of Robotics and Artificial Intelligence: The Power of the Covariance Matrix¶

Status: Alpha

Introduction¶

  • Introduction and Motivation

Kalman-Filter for State-Space-Estimation¶

Principle and 1D-Case¶

  • Bayes Filter
    • Foundations
      • Probabilities and Bayes Rule
      • Discrete Convolution
    • Bayes Filter
  • Principle of the Kalman Filter
    • Foundations
      • Probability Densities and Normal Distribution
      • Random Walk and Central Limit Theorem
      • Expectation
      • Estimators
      • Continuous Convolution
    • Principle of the Kalman Filter (1D case)

The Multi-Dimensional Kalman Filter for 1D-Kinematics¶

  • 1D-Kinematics of a Point Mass
  • State Space Model: From 1D-Kinematics to Matrices
  • Process Noise: Modeling Model Imperfection
    • Foundation: Multivariate Gaussian Distribution, Covariance Matrix and Correlation
    • Process Noise: Modeling Model Imperfection
  • Observation Model
  • Kalman Filter Summary

2D-Kinematics and the Extended Kalman Filter (EFK)¶

  • 2D-Kinematics
  • Rotations in 2D: Representing orientation and angular velocity
  • The Extended Kalman Filter: Handling the non-linearity of 2D-rotations
  • EKF for 2D Navigation
  • Observability
    • Foundation:
      • Column Space, Null Space, Rank of a Matrix
    • Observability

Advanced: 3D-Kinematics and the Error State Kalman Filter¶

  • Fundamentals: Quaternion kinematics for the error-state Kalman filter (external Link)
  • Example:
    • Generation of syntetic data
    • Implementation of a ESKF for 3D-Kinematics

Principle Component Analysis (PCA)¶

  • Principle Component Analysis
    • Foundations
      • Tensors and Transformation
      • Eigendecomposition, Eigenvalues and Eigenvectors
    • Principal Component Analysis
  • Application: PCA for Image Compression

Evolution Strategies for Optimization and Control¶

  • Introduction to Evolution strategy (ES)
  • Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
    • Foundations
      • Vector-form of the empirical Covariance Matrix
      • Exponential Smoothing
      • Hessian Matrix
      • Conjugate Directions
    • Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
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