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Coursera Robotics: Estimation and Learning

University of Pennsylvania via Coursera

Tags:
  • Overview
  1. Coursera
    Platform:
    Coursera
    Provider:
    University of Pennsylvania
    Length:
    4 weeks
    Effort:
    3-4 hours a week
    Language:
    English
    Credentials:
    Paid Certificate Available
    Part of:
    Robotics Specialization
    Overview
    How can robots determine their state and properties of the surrounding environment from noisy sensor measurements in time? In this module you will learn how to get robots to incorporate uncertainty into estimating and learning from a dynamic and changing world. Specific topics that will be covered include probabilistic generative models, Bayesian filtering for localization and mapping.

    Syllabus
    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.


    Taught by
    Dan Lee

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