Research on vehicle location of improved point cloud matching algorithm with laser odometer
Aiming at the problems that traditional iterative closest point(ICP)algorithm depends on initial position,long matching time and poor matching accuracy in the process of point cloud matching,a vehicle location method based on laser odometry improved point cloud registration algorithm was proposed.Firstly,point clouds were ordered and distorted point clouds were compensated in point cloud preprocessing.Then the static feature points were extracted stably after removing the dynamic feature points of the point cloud.Finally,in the process of point cloud registration,the point cloud was coarse registered to reduce the dependence of point cloud on the initial position,and then the bidirectional k-dimensional tree ICP algorithm was proposed for point cloud precise registration.Through the open source KITTI dataset and self-driving car platform for experimental test and analysis,the results show that compared with the traditional algorithm,the improved point cloud registration algorithm has faster matching speed and accuracy,small cumulative error of odometer trajectory.
NETL
NSTL
万方数据
维普
