Object Tracking and Localization

Learn how to locate an object and track it over time. These techniques are used in a variety of moving systems, such as self-driving car navigation and drone flight.

• Introduction to Motion
• This lesson introduces a way to represent motion mathematically, outlines what you'll learn in this section, and introduces optical flow.
• Robot Localization
• Learn to implement a Bayesian filter to locate a robot in space and represent uncertainty in robot motion.
• Mini-project: 2D Histogram Filter
• Write sense and move functions (and debug) a 2D histogram filter!
• Introduction to Kalman Filters
• Learn the intuition behind the Kalman Filter, a vehicle tracking algorithm, and implement a one-dimensional tracker of your own.
• Representing State and Motion
• Learn about representing the state of a car in a vector that can be modified using linear algebra.
• Matrices and Transformation of State
• Linear Algebra is a rich branch of math and a useful tool. In this lesson you'll learn about the matrix operations that underly multidimensional Kalman Filters.
• Simultaneous Localization and Mapping
• Learn how to implement SLAM: simultaneously localize an autonomous vehicle and create a map of landmarks in an environment.
  
• Optional: Vehicle Motion and Calculus
• Review the basics of calculus and see how to derive the x and y components of a self-driving car's motion from sensor measurements and other data.
• Project: Landmark Detection & Tracking
• Implement SLAM, a robust method for tracking an object over time and mapping out its surrounding environment, using elements of probability, motion models, and linear algebra.