The invention discloses a method for autonomously localizing robots on the basis of
laser radar. The method includes randomly generating N particles to form particle swarms around initial locations ofthe robots, and updating the particle swarms according to
robot real-time movement distances and real-time rotation angles measured by sensors of the robots at current operation moments of the robots; computing the superposition quantity of
point cloud of the
laser radar and obstacles of maps for each particle to use the superposition quantity as a
score of the particle, computing weighted position and posture average values of the particle swarms by the aid of the
score, which is used as a weight, of each particle and utilizing the weighted position and posture average values as AMCL (adaptive Monte Carlo localization)
estimation positions and posture; utilizing the AMCL
estimation positions and posture as initial values, acquiring scanned and matched positions and posture by the aid ofscanning and matching algorithms on the basis of Gauss-Newton iterative processes and utilizing the scanned and matched positions and posture as the optimal positions and posture of the robots at thecurrent operation moments; re-sampling the particle swarms by the aid of AMCL algorithms to ultimately obtain the
global optimal positions and posture of the robots during operation. The
global optimal positions and posture of the robots are used as localization results. The method has the
advantage that the localization convergence rate can be greatly increased, and the localization precision andthe localization stability can be greatly enhanced.