Kalman Filters: Estimation and Localization

In course

Ad adipisicing consectetur minim laborum proident proident aliqua amet consectetur eiusmod exercitation proident adipisicing officia. Esse tempor cupidatat eu occaecat ipsum do in. Esse magna est eiusmod cupidatat laboris ad. Duis eiusmod elit enim est. Magna do pariatur dolor consectetur proident sunt. Quis Lorem sunt do eu aute et anim occaecat.

Description

This lecture covers the theory and application of Kalman Filters for estimation and localization in robotics. Topics include time-discrete motion and measurement models, multi-dimensional Kalman Filters, components of a Kalman Filter, and working around Kalman Filter limitations with Particle Filters.

Instructor

cillum culpa sint

Consectetur eu elit nostrud non duis nisi. Dolor labore eu aliquip cillum pariatur officia est id minim et Lorem deserunt culpa ullamco. Commodo cupidatat culpa in deserunt nostrud mollit fugiat ex qui nulla laboris.

Official source

https://mediaspace.epfl.ch/media/0_1zaskzl3

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Ontological neighbourhood

Computer engineering

Computer architecture: Embedded systems

Computer programming: Topics in computer programming

Computer data storage: File systems

Theoretical computer science

Programming language theory: High-level programming languages

Related lectures (167)