Robots use SLAM (Simultaneous Localization and Mapping) algorithms to navigate by combining two main tasks: building a map of their surroundings and keeping track of their own location within that map at the same time. This dual task is essential for robots operating in unknown environments where GPS may not be available or reliable. SLAM enables robots to create an accurate representation of their environment and continuously update their position relative to that map as they move around.
In practice, a robot equipped with sensors, such as lidar or cameras, gathers information about nearby objects and features. The SLAM algorithm processes this sensor data to identify distinct landmarks in the environment. For example, it might recognize walls, tables, or furniture in a room. As the robot moves, it uses this information to create a map, while also applying mathematical techniques to estimate its position. The algorithm updates both the map and the robot's position iteratively, compensating for errors that accumulate over time due to imperfect sensor data and movements.
To illustrate how SLAM works, consider a warehouse robot tasked with inventory management. As it navigates through the aisles, it identifies different shelves and products using its sensors. The SLAM algorithm helps the robot build a detailed map of the warehouse layout while simultaneously tracking its location within that map. This allows the robot to move efficiently, avoid obstacles, and accurately return to specific locations to pick up or drop off items, demonstrating the practical benefits of SLAM in real-world applications.