Abstract: A point-plane based point cloud matching algorithm is proposed to deal with point cloud matching problem in 3D simultaneous localization and mapping(3D-SLAM).The 6DOF matching problem in 3D space are logically simplified as a 5DOF problem firstly.And then,the algorithm extracts break point in each row of laser data and employs an area growth method to find planes in point cloud.Normal vectors of planes are computed.The rotation of two frames can be estimated by comparing normal vectors of two associated planes in two frames.An improved leveled map algorithm is used to compute the translation parameter.Experiments on real data set containing high noise validate the proposed 3D-SLAM method.