English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 03m | 999 MB
Build software for an autonomous robot by implementing Python’s Kalman Filter on a self-driving car
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.
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
Table of Contents
01 Introduction
02 Free Installation Guide
03 Conda Environments
04 Filtering Basics
05 Kalman Filter Toy Implementation
06 Assignment 1 – Intro
07 Assignment 1 – Solution
08 Kalman Filter 1D Implementation
09 Assignment 2 – Intro
10 Assignment 2 – Solution
11 Kalman Filter 2D
12 Assignment 3 – Intro
13 Assignment 3 – Solution
14 Kalman Filter Prediction
15 Assignment 4 – Intro
16 Assignment 4 – Solution
17 Outro
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