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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Destrezas que aprenderás
Particle Filter, Estimation, Mapping
Reseñas
4.3 (494 calificaciones)
- 5 stars58,50Â %
- 4 stars20,85Â %
- 3 stars12,34Â %
- 2 stars4,04Â %
- 1 star4,25Â %
DA
5 de feb. de 2021
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!
NN
19 de jun. de 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 .
De la lección
Bayesian Estimation - Target Tracking
Impartido por:
Daniel Lee
Professor of Electrical and Systems Engineering
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