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 4 Introduction
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Kompetenzen, die Sie erwerben
Particle Filter, Estimation, Mapping
Bewertungen
4.3 (494Â Bewertungen)
- 5 stars58,50Â %
- 4 stars20,85Â %
- 3 stars12,34Â %
- 2 stars4,04Â %
- 1 star4,25Â %
NN
19. Juni 2016
This is course is really helpful for beginners to understand how probability is useful in Robotics.Assignments are bit tough but worth the time .
WW
8. Okt. 2020
The course is too difficult and the class is too short to understand, I have to spend a lot of this learn the knowledge needed in other place.
Aus der Unterrichtseinheit
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.
Unterrichtet von
Daniel Lee
Professor of Electrical and Systems Engineering
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