Practical Guide to SLAM¶
Who is this course for? This course is for robotics engineers and students who want to make their robot more capable at perceiving and navigating in an environment using SLAM, but have little to no prior experience with SLAM. Our goal is to give you the practical knowledge you need to run SLAM on a real robot, understand what the main pieces do, and tune them when things go wrong.
What is unique about this course? Many SLAM tutorials are either too simplified, treating it as a black box, or they are not beginner-friendly, loaded with lots of math. Although SLAM is an attractive topic, learning it can quickly become too theoretical and hard to follow. Here’s what’s different about this course:
- We keep it practical. In this course, SLAM is neither a black box nor a convoluted mathematical problem. We see it as a pipeline: some steps you should take to ensure high quality robot navigation, from choosing right sensors to evaluating the overall accuracy.
- We try to provide an interactive experience, where you can easily run a few SLAM methods and play with the knobs, while having a rough idea of what is happening in the code.
Course Features¶
Throughout this course, you will get:
| Feature | Description |
|---|---|
| Concepts | Introductions to SLAM and its components + information needed to use SLAM as a pipeline. |
| Interactive examples | Running SLAM and analyzing results on real sensor data captured by our robots. |
| Quizzes | Questions about the concepts with correct answers for your self-evaluation. |
| Exercises | Practicing with SLAM methods on different conditions for your self-evaluation. |
Prerequisites¶
Necessary background:
: You can refer to our course on Robot Operating System (ROS 2).
Recommended background:
- Docker: For easier access to practical examples without the need to install prerequisites.
Visual Studio Code: Using dev-containers inside VS Code for more stream-lined interaction with docker containers.- Linux: The preferred operating system for robotic developers with better compatibility with ROS.
- Theories: You can follow our course on Mathematical Foundations for Sensing & Perception.