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Starting the term 2 of Udacity Robotics Software Engineer Nanodegree today. Will be updating this page with micro breifs of topics and projects for the term.

This term focuses on localisation and mapping problems based on probabilistic and deep reinforcement learning models deployed on embedded hardware(NVIDIA Jetson Tx2).

High Level Topics

• Probabilistic Robotics.
• Introduction to Jetson
• Robotic System Deployment.
• Localization.
• Mapping and SLAM
• RL Basics
• Path Planning and RL Navigation.

The Jetson TX2

• What's in the box.

Interacting with the Hardware

• Breadboard and basic electronic circuits.

Robotics Sensor Options

• Cameras
• IMU : Magnetometers, Accelerometers, Gyroscopes.
• RADAR
• LIDAR
• IR
• Variable Resistors
  • Photoresistors
  • Thermistors
  • Magnetoresistor
  • Humistor
  • Force Sensitive Resistor

Robotic Inference

• Introduction to DIGITS environment
• Basic image classification over multiple catagories.
• Collect your own data and train a model for image classification.

Localization

• Algorithms
  • Extended Kalman Filters
  • Markov Localization
  • Grid Localization
  • Monte Carlo Localization

Kalman Filters

• Robot Uncertainity : Actuation and Measurment
• Sensor Fusion