Research On Point Cloud Matching Based Localization For Intelligent Vehicles

Research on intelligent vehicles is a cross domain which involves artificial intelligence,robotics,motion planning,control,etc.Among the techniques that make the vehicle intelligent,high-precision localization is basic and important.Because the environment intelligent vehicles travel in is priorly known in most cases,point cloud matching based localization has become the mainstream manner.Camera and LIDAR are two types of sensor which are used most widely in mapping and localization.As vision-based method is not robust to light condition variance,weather changes,etc,LIDAR based method becomes more and more favored by the researchers.For LIDAR based method,there are also some problems that need to be solved:It needs large storage to store the sensor map;The efficiency for environment representation is low;Map management is difficult;Map matching algorithm is not robust to local minimum,noise and outliers.In terms of these problems,a point cloud matching based localization and several point set registration algorithms for various scenes are proposed in this thesis.Details of these works are as following:In order to decrease storage needed to store the map,the stitched point cloud is post-processed to generate sensor map,precision and information of the map are not affected at the same time.For general environment,the point cloud is rasterized into 3D occupancy grid map which is stored in the form of octree.By doing this,it could not only decrease map storage,extract useful information,but also improve localization robustness via the multi-resolution octomap.In the structural areas,the octomap is compressed further to obtain compressed road scene map.Compared with raw point cloud,storage for compressed road scene map decreases to one thousand of the point cloud.This thesis gives the details of framework for point cloud matching based localization.The vehicle motion data are also fused and the map matching result is used as observation,which could improve robustness of localization.For the problem that the correspondence establishment relies heavily on initialization,a correspondence establishment algorithm between point sets based on graph isomorphism is proposed.The graph is established based on the geometric relations between points,after which the adjacency matrix could be obtained.Adjacency matrix of the two point sets are isomorphic,correspondence between points cloud be obtained through the permutation matrix between the two graphs.In light of the above,an optimization problem is constructed and solved by Monte Carlo method.In measurement update of the Monte Carlo optimization,weights of the particles are updated using Alternating Direction Method of Multipliers.Experimental results demonstrate that robustness to noise,outliers and misalignment initialization of the proposed methods outperforms the traditional methods with improvement of correspondence accuracy larger than50%.For the problem that the registration algorithm is not robust to noise,outliers,etc.,a point set registration algorithm based on Cubature Kalman Filter(CKF) is proposed.The point set registration is modeled using model space,then point cloud matching is converted to a filtering problem.Because the model is highly nonlinear,precision of the traditional methods is not high enough.Herein CKF is adopted to deal with the nonlinear part by numerical calculation.Compared with extended Kalman filter,unscented Kalman filter,CKF cloud approximate the nonlinearity for three orders.Experimental results show that the proposed method is robust to noise,outliers and misalignment initialization.It also outperforms some state-of-the-art methods in terms of accuracy and robustness.When the noise is 30%,the successful matching rate of the proposed method could be improved 20%.When the noise is 50%,the successful matching rate is improved by 30%.When the missing partial structures is 50%,the successful matching rate is improved by 30%.CKF based point set registration does not consider correlation between the error sources if there are more than one error source.Due to this,point set registration based on Cubature Split Covariance Intersection Filter(CSCIF) is proposed in this thesis.The proposed method divides covariance in filtering into two parts: independent and dependent,which are dealt with differently.By doing this,it is robust to two common errors in point set registration(independent error and dependent error).Compared with CKF based point set registration,Robustness to various types of error sources of CSCIF based point set registration has been improved substantially.Compared with state-of-the-art point set registration methods,robustness of the proposed method is improved further.When there are two types of error sources that exist simultaneously,error of CSCIF declines 40% compared with the CKF based point set registration.For the problem that the point set registration relies on the correctness percentage of point correspondence,a direct point set registration algorithm is presented in this thesis.The proposed method does not establish correspondence between point sets,but represents the point set using Gaussian Mixture Model(GMM).Point set registration is achieved by optimizing similarity of the two GMMs.Signature Quadratic Form Distance(SQFD)is used to measure the similarity between the two GMMs.Compared with L2 distance,K-L distance,kernel correlation function,etc.,SQFD computes expectation of the similarity function on the two input Gaussian kernels,which could measure similarity between GMMs better.For optimization of SQFD,Expectation Maximization is adopted in an iterative manner to guarantee convergence of the proposed algorithm.Experimental results demonstrate that precision of the GMM-SQFD based point set registration is improved compared with the correspondence-based point set registration.While the runtime complexity of GMM-SQFD based point set registration is higher than the correspondence-based point set registration.Thus,it is more suitable for the offline tasks,e.g.,map generation.