Autonomous Robots: Kalman Filter
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 2h 3m | 877 MB
Instructor: Daniel Stang

Build software for an autonomous robot by implementing Python's Kalman Filter on a self-driving car

Learn

Become proficient in using Kalman Filters
Solve real-world problems faced by self-driving cars or autonomous vehicles
Get an overview of the complete robotic software stack

About

In this course, you will learn not only how Kalman Filters work but also why they are needed. You will grips with writing the code to run the simulations designed to mimic a self-driving car. Don't worry if you don't have any experience in linear algebra or software; all the code in the course is written in Python, which is a very easy language to get up and running with, even if you're new to software programming.

This course provides simplified explanations of Kalman Filters. It also allows you to test your knowledge at the end of the course by working on simulators that the author has designed to cover a set of problems that any self-driving car can encounter. What's more? You will even get a working Kalman Filter code that you can deploy on a real robotic system.

Features

Get started with applying Kalman Filter and toy implementation
Implement 1D and 2D depth fields in Kalman Filter
Understand the filter's essence, its meanings, and complex applications



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