About this course: 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.
Created by: University of Pennsylvania
Taught by: Daniel Lee, Professor of Electrical and Systems Engineering
School of Engineering and Applied Science
| Basic Info |
Course 5 of 6 in the
Robotics Specialization.
|
| Commitment | 4 weeks of study, 3-4 hours/week |
| Language | English, Subtitles: Chinese (Simplified) |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
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...
9 videos, 3 readings
Graded: Color Learning and Target Detection
WEEK 2
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-...
5 videos
Graded: Kalman Filter Tracking
WEEK 3
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.
6 videos
Graded: 2D Occupancy Grid Mapping
WEEK 4
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.
6 videos
Graded: Particle Filter Based Localization
FAQs
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University of Pennsylvania
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
Pricing
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Ratings and Reviews
Pretty practical course It' ll involve a good amount of programming. Not quiz and theoretical verification here.
Leanring of mechanism and implementation of Kalman filter and particle filter from experiment is very interesting for me. And these method let me know more about map building in SLAM framework.
Here I learnt all the building blocks of Probabilistic Robotics and the significance of statistical methods etc to deal with the the non-linear world.
The course content is very concise and to the point. And, the knowledge transferred is well structured.
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周
希望理论部分讲的再深入些！