SLAM: Real-Time Localization and Mapping

SLAM (Simultaneous Localization and Mapping) is a fundamental technology that enables robots to simultaneously locate themselves and construct environmental maps in unknown settings. This technique is widely applied in autonomous driving, drone navigation, service robotics, and other fields. From an implementation perspective, SLAM systems typically involve sensor data acquisition modules, state estimation algorithms, and map management components.

The Extended Kalman Filter (EKF) serves as a classical state estimation algorithm for addressing localization challenges in nonlinear systems. In SLAM implementations, EKF continuously updates the robot's position and map feature estimates by fusing multi-sensor data (such as LiDAR, IMU, and visual inputs). The algorithm implementation involves maintaining a state vector containing robot pose and landmark positions, with covariance matrices representing estimation uncertainties. Key functions include motion model prediction (using odometry or control inputs) and measurement updates through sensor observations.

The core workflow can be summarized as: Prediction Phase: The robot predicts its new position based on motion models while updating covariance matrices to reflect increased uncertainty. Code implementations typically use kinematic equations with Gaussian noise models. Observation Phase: Sensors collect environmental data (e.g., feature points) which are matched against existing map features, with observation errors calculated through measurement models. Update Phase: Observation data corrects predictions using Kalman gain computations, optimizing both localization accuracy and map precision through covariance updates.

SLAM system challenges include efficient handling of sensor noise, data association problems (correctly matching observed features with map landmarks), and computational optimization for real-time performance. Modern SLAM systems increasingly incorporate particle filters (for non-Gaussian distributions) and graph optimization techniques (like g2o framework implementations) to enhance robustness and accuracy through nonlinear optimization of pose graphs.