Research On Key Technologies Of Simultaneous Localization And Mapping For Intelligent Vehicle

Intelligent vehicle(IV)is a major part of intelligent transportation system.In order to completely emancipate human beings from the heavy driving process and effectively avoid traffic accidents,intelligent vehicle needs to be achieved self-driving.The simultaneous localization and mapping(SLAM)is a key technology of autonomous navigation for intelligent vehicle,it can provide the necessary conditions for the vehicle to realize self-driving in an unknown environment where the positioning system fails.Hence,from three aspects,the SLAM method based on probability,the data association method of SLAM and the SLAM method based on scan matching,respectively,the key technologies of SLAM for intelligent vehicle are studied.The major contents are summarized as follows:(1)Due to the SLAM system of intelligent vehicle,the coordinates needed in the research of SLAM is defined.The vehicle kinematic model,sensor observation model,environmental map model and data association model are developed.The probability model of the SLAM problem for intelligent vehicle is given based on the above models,which have built a unified platform for the research on key technologies of SLAM.(2)Aiming at the problem of extracting the natural entity landmark in the geometric feature map,a circular feature extraction method based on laser radar data is studied.The center and diameter of the entity landmarks are extracted based on the distance information and angle information from laser radar data in the proposed extraction method.The effectiveness of circular feature extraction is verified by Victoria Park dataset,which provides a feature extraction method for building geometric map in subsequent SLAM algorithm.Due to the SLAM algorithm based on the extended Kalman filter is susceptible to the uncertainty of the nonlinear model and the uncertainty of the error statistics.Based on the idea of strong tracking filter,an adaptive fading EKF-SLAM algorithm is proposed.This proposed algorithm lays a theoretical foundation for the research of the core algorithms in subsequent chapters.(3)In order to solve the problem of poor consistency and the estimation accuracy decreasing with the degradation and impoverishment of particles in FastSLAM,a FastSLAM algorithm based on the improved proposal distribution and partial resampling strategy is proposed.In the proposed algorithm,the strong tracking square root central difference Kalman filter is designed.STSRCDKF is used to design an adaptive adjusting proposal distribution of the particle filter in the estimation stage of vehicle's pose.Because the proposal distribution is very close to the posterior probability distribution of particles,hence the sampling precision of particle is improved.In the stage of map estimation,STSRCDKF is used to estimate the environmental landmark to improve the accuracy of building map.In the resampling stage,a partial resampling strategy is adopted to reduce the degradation and impoverishment of the particle set,and the consistency of the algorithm is improved.The experimental results demonstrate the advantages of the proposed algorithm in robustness,consistency and estimation accuracy.(4)In SLAM,data association is the precondition and basis of state estimation,which is the core and key to ensure the convergence of localization and mapping.In order to solve the problem that the association algorithms used in SLAM can not simultaneously guarantee low computational complexity and high association correct rate.Two different joint data association methods are proposed.Firstly,a data association method based on clustering strategy and central difference joint compatibility criterion is proposed based on joint compatibility branch and bound(JCBB)algorithm.This method effectively solves the problem that JCBB algorithm is susceptible to linearization error and high complexity.It can reduce the complexity of the SLAM algorithm while obtaining the accurate association results.Secondly,according to the joint maximum likelihood criterion,the SLAM data association problem is transformed into a combinatorial optimization problem.An artificial fish swarm algorithm based on jump behavior and adaptive step-size is applied to search for the optimal data association solution.The experimental results show that the proposed association methods can provide a reliable guarantee for improving the real-time and accuracy of SLAM for intelligent vehicle.(5)In order to realize vehicle's self-localization and build a dense feature map for describing the details of the environment,a SLAM method based on scan matching and particle filter is proposed.Strong tracking square root central difference particle filter is used to fuse location results based on ICP matching and location results based on odometer.The effect of cumulative error on vehicle's pose estimation and map updating is effectively avoided in the process of scan matching.In the construction stage of point-cloud map,the corresponding relationship between the current scan points and the reference points is found,and the points that have corresponding relationships are fused according to their respective weight values.Finally,the point-cloud map stitching is completed based on the global pose of the vehicle.Experimental results show that the proposed method can accurately estimate vehicle's pose,and the point-cloud map can provide detailed environmental information for autonomous driving of intelligent vehicle.