How do robots perform localization and mapping (SLAM)?

Robots perform Simultaneous Localization and Mapping (SLAM) by combining sensor data and algorithms to build a map of an unknown environment while tracking their position within it. The core idea is to solve two interdependent problems: estimating the robot’s location (localization) and constructing a representation of the surroundings (mapping). Sensors like LiDAR, cameras, inertial measurement units (IMUs), or wheel encoders provide raw data about the robot’s motion and environment. For example, a LiDAR sensor might measure distances to walls, while wheel encoders track how far the robot has moved. Algorithms process this data to infer the robot’s trajectory and update the map incrementally. Key to this process is handling uncertainty—no sensor is perfect, so probabilistic methods like Kalman filters or particle filters are often used to model noise and improve estimates over time.

SLAM implementations typically rely on probabilistic models and optimization techniques. A common approach is to represent the environment as a graph, where nodes represent the robot’s poses (positions and orientations) and edges represent constraints between poses based on sensor data. For instance, if a robot detects a recognizable feature (like a door or corner) with its camera, it can link that observation to previous sightings to correct its estimated position. Modern systems like ORB-SLAM use visual features (e.g., corners or edges in images) as landmarks, tracking them across frames to refine the map. Real-time operation is achieved by prioritizing computational efficiency—for example, using sparse feature matching instead of processing every pixel. Loop closure detection, where the robot recognizes a previously visited location, is critical for correcting accumulated errors. This might involve comparing current sensor data to stored map features and adjusting the graph to align inconsistencies.

Practical SLAM systems balance accuracy, speed, and resource usage. For example, autonomous vacuums often use LiDAR-based SLAM (like the algorithm in the Roomba i7) to create 2D floor plans, while drones might use visual-inertial SLAM (e.g., VINS-Fusion) combining cameras and IMUs for 3D mapping. Developers must consider trade-offs: dense mapping (e.g., with RGB-D sensors) provides detailed environments but requires more processing, while sparse methods are faster but less precise. Open-source libraries like ROS’s GMapping or Google’s Cartographer abstract low-level complexity, letting developers focus on tuning parameters like sensor noise models or loop closure thresholds. Challenges include handling dynamic objects (e.g., moving people) and ensuring robustness across lighting or terrain changes. By integrating sensor data, probabilistic reasoning, and efficient optimization, SLAM enables robots to operate autonomously in diverse environments.