The invention discloses an
urban environment composition method for unmanned vehicles. Under the condition of being independent of external positioning sensors such as speedometers, GPS and inertial navigators, the trajectory tracking and environment map building of an unmanned vehicle are completed by just using few particles for 3D
laser-
point cloud data returned by a vehicle-mounted
laser radar, thereby providing basis for the autonomous running of the
unmanned ground vehicle in an unknown environment; and according to the invention, an ICP
algorithm is applied to adjacent two frames of data so as to obtain a coarse
estimation on the real position and posture of a vehicle, and then redundance is performed near the coarse
estimation based on
gaussian distribution. Although the coarse
estimation is not the real position and posture of the vehicle, the coarse estimation is a high-probability area of the real position and posture of the vehicle, so that an effect of relatively accurate positioning and composition is achieved by using a small amount of particles in the process of subsequent redundance, thereby avoiding the fitting of a vehicle trajectory by using a large amount of particles in a traditional method, improving the efficiency of the
algorithm, and effectively restraining a phenomenon of particle degeneracy caused by bad particle estimation.