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Robotics: Estimation and Learning

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  • Gaussian Model Learning
    • We will learn about the Gaussian distribution for parametric modeling in robotics. The Gaussian distribution is the most widely used continuous distribution and provides a useful way to estimate uncertainty and predict in the world. We will start by discussing the one-dimensional Gaussian distribution, and then move on to the multivariate Gaussian distribution. Finally, we will extend the concept to models that use Mixtures of Gaussians.
  • Bayesian Estimation - Target Tracking
    • We will learn about the Gaussian distribution for tracking a dynamical system. We will start by discussing the dynamical systems and their impact on probability distributions. This linear Kalman filter system will be described in detail, and, in addition, non-linear filtering systems will be explored.
  • Mapping
    • We will learn about robotic mapping. Specifically, our goal of this week is to understand a mapping algorithm called Occupancy Grid Mapping based on range measurements. Later in the week, we introduce 3D mapping as well.
  • Bayesian Estimation - Localization
    • We will learn about robotic localization. Specifically, our goal of this week is to understand a how range measurements, coupled with odometer readings, can place a robot on a map. Later in the week, we introduce 3D localization as well.