Active SLAM

Active SLAM (Simultaneous Localization and Mapping) focuses on intelligently controlling a robot's movement to optimize map creation and localization accuracy in unknown environments. Current research emphasizes efficient exploration strategies, often employing graph-based SLAM backends and information-theoretic approaches like D-optimality or entropy maximization to guide robot navigation. This active approach improves the efficiency and robustness of SLAM, with applications ranging from autonomous exploration in challenging terrains to collaborative mapping using multiple robots.