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.
Overview
Syllabus
- 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.
Taught by
Cezanne Camacho (nd891), Sebatian Thrun and Andy Brown
