Adaptive Monte Carlo Localization

Adaptive Monte Carlo Localization (AMCL) is a technique used by robots to determine their position and orientation in an environment. It combines sensor data and a map of the surroundings. AMCL uses a set of "particles," or possible location estimates, which are updated based on new information. Over time, it refines these estimates to find the most likely position of the robot. The "adaptive" aspect means the method adjusts the number of particles based on the certainty of the robot's location, improving efficiency and accuracy in various environments, including complex and dynamic ones.