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The Duckietown Instructor Manual - ente
The Duckietown Instructor Manual
Introduction
Who Should Read This Book?
How to Get Started
How to Use This Book
Quickstart Guide
The Professor’s journey
Available Resources
Overview
Lecture Slides and Recordings
Introductions to Duckietown
Getting started
Autonomy and Automation
Autonomous Vehicles
Robots
Architectures in Robotics
Testing
Signal Processing
Networking
Full Introductory Lectures
Tools
Containerization
Middlewares and the Robot Operating System (ROS)
Git
Modeling
Robot Representations
Modeling a Differential Drive Robot
Full Lecture on Modeling
Control Systems
Introduction to Control Systems
Odometry
PID Control
Full Lecture on Control
Computer Vision
Projective Geometry
Camera Calibration
Image Filtering
Full Lectures on Computer Vision
Visual Perception
Introduction to Visual Perception
Introduction to Neural Networks
Deep Convolutional Neural Networks
Object Detection
Full Lectures on Visual Perception
State Estimation
Probabilistic Representations
Bayes Filter
Kalman Filter
Particle Filter
Histogram Filter
Full Lecture on Filtering
Robust Estimation
Full Lecture on Simultaneous Localization and Mapping
Planning
Motion Planning
Planning on Graphs
Sampling-based Planning
Full Lecture on Planning
End-to-End Learning Approaches
Markov Decision Processes
Policy Iteration
Q Learning
Simulation and Sim-to-real
Full Lecture on Reinforcement Learning
Imitation Learning
Advanced Topics
Multi-vehicle Planning
Safety
Estimating Uncertainty
Estimation from Motion Blur