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.
WEEK 2 Introduction
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学習するスキル
Particle Filter, Estimation, Mapping
レビュー
2016年6月19日
This is course is really helpful for beginners to understand how probability is useful in Robotics.Assignments are bit tough but worth the time .
2016年6月24日
A tough course with few hours of lecture material and some good programming assignments.You will be satisfied by those assignments however .
レッスンから
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.
講師
Daniel Lee
Professor of Electrical and Systems Engineering
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