Targeting the positioning requirements of autonomous vehicles in scenarios with insufficient global navigation satellite system(GNSS)signals,a tightly-coupled simultaneous localization and mapping(SLAM)algorithm that integrates LiDAR,inertial measurement unit(IMU)and vehicle kinematic constraints is proposed.First,vehicle kinematic constraints are established with the angular rate of IMU,rear wheel speed,and front wheel angle.By decoupling the displacement and orientation information of the vehicle motion,the constraints for displacement and orientation are formulated separately to enhance the accuracy of optimization.Then,adaptive weights are introduced based on the number of feature points and the steering angle to dynamically adjust the weight of the vehicle kinematic constraints in real time.Finally,an odometer is constructed based on the angular rate of IMU and rear wheel speed,providing precise initial values for the back-end tightly-coupled optimization and effectively preventing falling into local optima.Results from tests conducted in various road scenarios demonstrate compared with the algorithms of LeGO_LOAM and LIO_SAM,the average planar positioning accuracy of the proposed algorithm is improved by 32%and 29%respectively,which provides a short-term high-precision positioning solution for autonomous vehicles in situations where GNSS signals are insufficient.
autonomous drivingsimultaneous localization and mappingmulti-sensor fusionvehicle kinematics
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