SLAM Explained: How Robots Build Maps and Find Themselves

Simultaneous Localization and Mapping (SLAM) is a classic chicken-and-egg problem in robotics: to build a map, you need to know your exact location, but to know your exact location, you need a map. SLAM algorithms solve both problems simultaneously.

How SLAM Works

As a robot moves through an environment, it uses odometry (wheel encoders, IMUs) to estimate its motion. However, odometry drifts over time. SLAM corrects this drift by looking at external sensor data—typically from a 2D or 3D LiDAR, or depth cameras—and identifying features (landmarks) in the environment. When the robot sees a landmark it recognizes from a different angle, it can mathematically correct its estimated position.

Common SLAM Approaches

• EKF-SLAM: Uses an Extended Kalman Filter. It is mathematically elegant but scales poorly in very large environments.
• Particle Filters (FastSLAM): Represents the robot's possible locations as a cloud of 'particles'. Highly robust for 2D mapping.
• GraphSLAM: The modern standard for large-scale mapping. It formulates the problem as a graph of poses and constraints, optimizing the entire trajectory to minimize error.

Getting Started with SLAM

If you want to implement SLAM, you should learn ROS 2 (Robot Operating System). Packages like `slam_toolbox` provide robust, out-of-the-box implementations. For a practical application, check out our guide on building an obstacle avoidance robot.