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
4.3 (480 件の評価)
2021年2月5日
This course was interesting but I think the video material was too shallow and not detailed enough. The assignment for Week 4 was extremely challenging!
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 .
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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