Urban environment composition method for unmanned vehicles

Abstract

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.

Description

technical field [0001] The invention relates to the technical field of positioning and composition based on unmanned ground vehicles, in particular to an urban environment composition method for unmanned vehicles. Background technique [0002] As a research hotspot in the field of intelligent robotics, unmanned ground vehicles have important application prospects in intelligent transportation systems and military security. Unmanned vehicles are intelligent. They can assist driving when the driver is drunk or fatigued, remind them in time of operating errors, and issue an alarm in time when a vehicle hardware or software failure is detected, so as to reduce traffic accidents and improve traffic safety. Unmanned vehicles in the military field can replace people to perform tasks in dangerous places and complete field material transportation, ensuring the safety of soldiers and reducing casualties. [0003] Positioning and composition are important research contents in the fiel...

AI Technical Summary

Problems solved by technology

EKF and RBPF are widely used as classic algorithms in the SLAM field, but the EKF algorithm is based on the strong assumption that the robot motion model and sensor noise are Gaussian distributions. When this assumption is not satisfied, the filter will diverge. This assumption is not satisfied in most cases, which limits the application range of the algorithm

The RBPF algorithm uses a large number of particles to fit the trajectory of the mobile robot, without relying on any external assumptions, but there are problems such as large number of particles, complex calculation, lack of particles, and closed loop problems.

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