Robots perform localization and mapping through a process known as Simultaneous Localization and Mapping (SLAM). This technique enables a robot to create a map of an unfamiliar environment while keeping track of its own position within that environment. Essentially, SLAM combines sensor data with algorithms that handle both mapping and localization tasks at the same time, allowing the robot to navigate and understand its surroundings effectively.
The process begins with a robot equipped with sensors, such as LiDAR, cameras, or ultrasonic sensors, that collect data about the environment. As the robot moves, it gathers information about obstacles, surfaces, and other features in its vicinity. The data from these sensors is then processed using various algorithms. For instance, an extended Kalman filter or particle filtering techniques may be employed to estimate the robot's position and orientation relative to the constructed map. By continuously updating this information, the robot can adjust its understanding of both the map and its location as it explores new areas.
A practical example of SLAM can be seen in autonomous vacuum cleaners. These devices use multiple sensors to detect room layouts and obstacles, allowing them to build a map of the home while navigating. As they clean, they update their position on this map, ensuring they cover all areas without getting stuck or missing spots. Another example is in the field of autonomous vehicles, which use SLAM to identify road features and obstacles while constantly determining their position on the road. This dual ability to build and navigate a map is what makes SLAM a crucial component in robotics.