Robot SLAM and Localization
How Robots Build Maps and Find Themselves
Course Overview
This course takes you deep into the world of SLAM and robot localization using ROS 2. You'll start by understanding the core theory behind SLAM — how robots build maps and locate themselves simultaneously — then move into hands-on implementation using SLAM Toolbox and AMCL. By the end, your robot will be able to map any environment and localize itself precisely within it.
Who is this for: ROS 2 developers who completed a robot modeling/simulation course and are ready to give their robot the ability to map and navigate its environment.
What You'll Learn
Understand the theory behind SLAM and occupancy grid maps
Set up and configure SLAM Toolbox for real-time mapping
Save, serialize, and manage maps for reuse
Implement AMCL for precise robot localization
Configure the particle filter and odometry parameters
Use Nav2 stack components for a full localization pipeline
Curriculum Explorer5 Sections • 45 Lessons • 10h 52m
hardware Requirements
Completion of the Robot Modeling and Simulation course (or equivalent experience)
Solid understanding of ROS 2 (nodes, topics, services, launch files)
Familiarity with Gazebo simulation environment
Basic knowledge of Linux and terminal commands
A computer running Ubuntu 22.04
No prior experience with SLAM or localization required
Your Instructor
Kyrillos Fekry
Senior Robotics Software Engineer
"Senior Robotics Software Engineer and Systems Architect with over Five years of experience leading the design, architecture, and production deployment of autonomous mobile robot platforms. Demonstrated ability to remotely lead cross-functional engineering teams, architect scalable robot software systems—from behavior engines and navigation stacks to CI/CD pipelines and mass-production workflows."
Robotics Engineering
Certificate of Completion
Official industry-recognized credential upon finishing.
