Map the Unknown
SLAM, occupancy grids, localization, and loop closure.
What You'll Learn
- Explain the SLAM problem and why it is fundamentally challenging
- Build an occupancy grid map from laser scan data
- Understand localization techniques: particle filters and scan matching
- Describe graph-based SLAM and how loop closure corrects drift
- Evaluate when to use lidar SLAM vs visual SLAM vs GPS
Why Mapping Matters
Understand why robots need maps, explore different types of maps, and learn the difference between mapping and navigation with pre-existing maps.
Occupancy Grid Mapping
Learn how robots represent space as grids, update cell probabilities using sensor data, and use log-odds representation for efficient probabilistic mapping.
Localization Basics
Discover how robots figure out their position on a known map using particle filters and Monte Carlo localization to handle uncertainty.
SLAM Explained
Understand the chicken-and-egg problem of simultaneous localization and mapping, explore graph-based and feature-based SLAM approaches, and see how modern algorithms solve this fundamental robotics challenge.
Loop Closure
Learn how recognizing previously visited places corrects accumulated drift, enables global map consistency, and transforms SLAM from a short-term to a long-term solution.