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Robot Operating System (ROS)
ROS2 Self Driving Car with Deep Learning and Computer Vision

ROS2 Self Driving Car with Deep Learning and Computer Vision

Loại khoá học: Software Engineering

Autonomous Car using TensorFlow and Neural Networks for Beginners

image
50.000 VND
119 VND
Đầy Đủ Bài Giảng
Học Online Tiện Lợi
Kích Hoạt Nhanh 2-5 Phút
Thanh toán tự động
Được phép tải xuống

Tổng quan

Nội dung khoá học

Đánh giá

Mô tả

This Course Contains ROS2 Based self-driving car through an RGB camera, created from scratch


Self Drive Features:

- Lane Assist

- Cruise Control

- T-Junction Navigation

- Crossing Intersections


Ros Package

  • World Models Creation

  • Prius OSRF gazebo Model Editing

  • Nodes, Launch Files

  • SDF through Gazebo

  • Textures and Plugins in SDF


Software Part :

  • Perception Pipeline setup

  • Lane Detection with Computer Vision Techniques

  • Sign Classification using (custom-built) CNN

  • Traffic Light Detection Using Haar Cascades

  • Sign and Traffic Light Tracking using Optical Flow

  • Rule-Based Control Algorithms

Pre-Course Requirments

Software Based

  • Ubuntu 20.04 (LTS)

  • ROS2 - Foxy Fitzroy

  • Python 3.6

  • Opencv 4.2

  • Tensorflow 2.14

Skill Based

  • Basic ROS2 Nodes Communication

  • Basic CV knowledge

  • Launch Files

  • Gazebo Model Creation

  • Motivated mind :)


Course Flow (Self-Driving [Development Stage])

We will quickly get our car running on Raspberry Pi by utilizing 3D models ( provided in the repository) and car parts bought from links provided by instructors. After that, we will interface raspberry Pi with Motors and the camera to get started with Serious programming.


Then by understanding the concept of self-drive and how it will transform our near future in the field of transportation and the environment. Then we will perform a comparison between two SD Giants (Tesla & Waymo) ;). After that, we will put forward our proposal by directly talking you inside the simulation so that you can witness course outcomes yourself.

Primarily our Self Driving car will be composed of four key features.

                      1) Lane Assist                              2) Cruise Control                     

                      3) Navigating T-Junction             4) Crossing Intersection

Each feature development will comprise of two parts

a) Detection: Gathering information required for that feature

b) Control:  Proposing appropriate response for the information received


Software Requirements

  • Ubuntu 20.4 and ROS2 Foxy

  • Python 3.6

  • OpenCV 4.2

  • TensorFlow

  • Motivated mind for a huge programming Project

    - Before buying take a look into this course Github repository  or message

    ( if you do not want to buy get the code at least and learn from it :) )

Bạn sẽ học được gì

Build your own Self Driving Car in Simulation (ROS2)

Learn to develop 4 Essential Self Drive features (Lane Assist, Cruise Control, Nav. T-Junc, Cross Intersections)

Master ComputerVision techniques e.g. (Detection, Localization, Tracking)

Deep Dive with Custom-built Neural Networks (CNN's)

( NEW!!! ) Develop a Satellite Navigation System (i.e GPS ) that helps the SDC navigate to any desired destination autonomously.

Learn how to utilize functionality provided by other repos for your needs through a Practical example.

Yêu cầu

  • Python basic Programming and Modules
  • ROS2 Basic Nodes and Launch Files Processing
  • Gazebo Models Communication with ROS
  • Basic Opencv Processing

Nội dung khoá học

10 sections

Prerequisite

6 lectures
Guide 1: ROS2 on Linux Installation and Path setup
04:16
Guide 2: Run Self Driving Car Project in Docker Windows and Linux
00:30
Docker Project Execution
07:04
Guide 3: How to Run the Project
11:04
Guide 4 : How to Live Code each Self Drive Feature
05:12
Github Repositories and its Branches
00:13

ROS2 package and Gazebo track model setup

7 lectures
Course Walk through
04:33
Package creation and sourcing
09:33
Repository Update and Understanding
00:03
Sign board meshes
08:31
Gazebo joints in meshes
07:02
Track Creation
08:45
Model Textures
02:16

Prius car and sign board models setup

9 lectures
Sign board Textures
08:43
Light plugins
02:04
Light plugins setting up
06:26
Gazebo Ros 2 Interfacing
09:01
Prius car package
01:07
Prius driving
01:25
Camera sensors and working
01:13
Camera basic setup
12:24
Adding camera to Prius
14:10

Ros2 car interfacing Nodes and World setup

12 lectures
Video node working
04:16
Video recording Node
13:40
How car drives
03:00
Node creation to Drive the Car
10:49
Meshes theory to spawn
02:57
Spawning Models
08:18
Computer Vision Node
2 questions
Organizing Models directories
07:33
What is a Bash File
03:56
Automating Command
06:17
World setup
13:03
Further Development
00:06

Inception

4 lectures
What is SelfDrive?
02:25
Waymo Vs Tesla
02:57
Proposal
04:48
Process Breakdown
03:42

Self Drive : Feature 1 ( Lane Assist )

14 lectures
Detection : CourseFlow
00:48
Detection : Overview
01:09
Detection : Stage 1 [ Lane Segmentation ] (Theory)
04:49
Detection : Stage 1 [ Lane Segmentation ] (Coding)
30:52
Detection : Stage 2 [ Why Estimation ] (Theory)
01:29
Detection : Stage 2 [ Custom Estimation Algo. ] (Theory)
02:14
Detection : Stage 2 [ Custom Estimation Algo. ] (Coding)
20:12
Detection : Stage 3 [ Cleaning ] (Theory)
05:58
Detection : Stage 3 [ Cleaning ] (Coding)
13:51
Detection : Stage 4 [ Data Extraction ] (Theory)
05:08
Detection : Stage 4 [ Data Extraction ] (Coding)
19:05
Control : CourseFlow
00:23
Control : Goal And Constraints
01:20
Control : Lane Assist (Coding)
20:46

Self Drive : Features (Cruise Control & T Junc. Navigation)

17 lectures
Detection : CourseFlow
00:39
Detection : Overview
00:55
Detection : Detection & its Stages
00:32
Detection : Stage 1 [ Localization ] (Theory)
03:59
Detection : Stage 1 [ Localization ] (Coding)
11:43
Detection : Stage 2 [ Classification ] (Theory)
11:00
Detection : Stage 2 [ Classification ] Building Custom CNN (Theory)
11:26
Detection : Stage 2 [ Classification ] Building Custom CNN (Coding)
07:34
Detection : Stage 3 [ Tracking ] (Theory)
12:12
Detection : Stage 3 [ Tracking ] (Coding)
13:40
Control : CourseFlow
00:29
Control : [ Cruise Control ] Goal And Constraints (Theory)
00:47
Control : [ Cruise Control ] Proposed Algorithm (Theory)
01:46
Control : [ Cruise Control ] (Coding)
04:57
Control : [ T-Junc Nav, ] Goal And Constraints (Theory)
00:30
Control : [ T-Junc Nav. ] Proposed Algorithm (Theory)
01:35
Control : [ T-Junc Nav. ] (Coding)
04:05

Self Drive : Feature 4 (Cross Intersection)

18 lectures
Detection : CourseFlow
00:29
Detection : Why detect Traffic Lights?
00:33
Detection : Why not imitate sIgn detection methodology?
01:24
Detection : Stage 1 [Traffic Light Detection] Haar Cascades (Theory)
09:00
Detection : Stage 1 [Traffic Light Det.] Haar Cascades (Training - Linux)
19:05
Detection : Stage 1 [Traffic Light Det.] Haar Cascades (Training - Windows)
23:01
Detection : Stage 1 [Traffic Light Det.] Haar Cascades (Integrating - Linux)
02:04
Detection : Stage 2 [ Confirmation And State Ret. ] (Theory)
02:27
Detection : Stage 2 [ Confirmation And State Ret. ] (Coding)
06:42
Detection : Stage 3 Why Track Traffic Light? (Theory)
01:03
Detection : Stage 3 Is Tracking Enough? (Theory)
02:07
Detection : Stage 3 Creating Tracker Class (Coding)
10:18
Detection : Stage 3 Integrating Tracker (Coding)
05:18
Detection : Process Flow
01:29
Control : CourseFlow
00:17
Control : Goal And Constraints
00:41
Control : Proposed Algorithm
02:12
Control : Crossing Intersection (Coding)
03:12

Self Drive : Feature 5 (Satellite Navigation) [NEW!!!]

12 lectures
Guide to Run the Feature!
00:03
Why Sat-Nav ? + Sneak Peek of the Feature !
05:56
Implementation Overview
02:53
SAT-NAV : Stage 1 [ Localization ] (A)
09:43
SAT-NAV : Stage 1 [ Localization ] (B)
16:01
SAT-NAV : Stage 1 [ Localization ] (C)
18:35
SAT-NAV : Stage 2 [ Mapping ] (A)
10:34
SAT-NAV : Stage 2 [ Mapping ] (B)
22:58
SAT-NAV : Stage 3 [ Path-Planning ]
07:08
SAT-NAV : Stage 4 [ Motion-Planning ] (A)
14:25
SAT-NAV : Stage 4 [ Motion-Planning ] (B)
21:24
SAT-NAV : Stage 4 [ Motion-Planning ] (C)
18:29

Conclusion :)

1 lectures
What We Achieved!
00:48

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